Methods and apparatus for characterization of tissue samples

ABSTRACT

The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.

RELATED APPLICATIONS

[0001] This application is related to the following commonly-ownedapplications: Attorney Docket No. MDS-035, entitled, “Methods andApparatus for Characterization of Tissue Samples”; Attorney Docket No.MDS-035A, entitled, “Methods and Apparatus for Displaying DiagnosticData”; Attorney Docket No. MDS-035B, entitled, “Methods and Apparatusfor Visually Enhancing Images”; Attorney Docket No. MDS-035E, entitled,“Methods and Apparatus for Processing Image Data for Use in TissueCharacterization”; Attorney Docket No. MDS-035F, entitled, “Methods andApparatus for Processing Spectral Data for Use in TissueCharacterization”; Attorney Docket No. MDS-035G, entitled, “Methods andApparatus for Evaluating Image Focus”; and MDS-035H, entitled, “Methodsand Apparatus for Calibrating Spectral Data,” all of which are filed oneven date herewith.

FIELD OF THE INVENTION

[0002] This invention relates generally to the classification of tissue.More particularly, in certain embodiments, the invention relates to thecombination of statistical and/or non-statistical tissue classificationschemes.

BACKGROUND OF THE INVENTION

[0003] It is common in the field of medicine to perform visualexamination to diagnose disease. For example, visual examination of thecervix can discern areas where there is a suspicion of pathology.However, direct visual observation alone may be inadequate for properidentification of an abnormal tissue sample, particularly in the earlystages of disease.

[0004] In some procedures, such as colposcopic examinations, a chemicalagent, such as acetic acid, is applied to enhance the differences inappearance between normal and pathological tissue. Such acetowhiteningtechniques may aid a colposcopist in the determination of areas in whichthere is a suspicion of pathology.

[0005] Colposcopic techniques are not perfect. They generally requireanalysis by a highly-trained physician. Colposcopic images may containcomplex and confusing patterns and may be affected by glare, shadow, orthe presence of blood or other obstruction, rendering an indeterminatediagnosis.

[0006] Spectral analysis has increasingly been used to diagnose diseasein tissue. Spectral analysis is based on the principle that theintensity of light that is transmitted from an illuminated tissue samplemay indicate the state of health of the tissue. As in colposcopicexamination, spectral analysis of tissue may be conducted using acontrast agent such as acetic acid. In spectral analysis, the contrastagent is used to enhance differences in the light that is transmittedfrom normal and pathological tissues.

[0007] Spectral analysis offers the prospect of at leastpartially-automated diagnosis of tissue using a classificationalgorithm. A typical tissue classification algorithm applies a singlestatistical technique to determine the probability that data from atissue sample falls within a certain predetermined class. The result maybe inaccurate, and may vary depending on the assumptions of thestatistical technique applied. Furthermore, examinations using spectralanalysis may be adversely affected by glare, shadow, or the presence ofblood or other obstruction, rendering inaccurate tissue-classprobabilities.

[0008] Thus, there exists a need for an improved method of determiningtissue-class probabilities for a tissue sample.

SUMMARY OF THE INVENTION

[0009] The invention provides methods for determining a tissue-classprobability of a region of a tissue sample. A tissue-class probabilityis a probability that a given region of a tissue sample contains tissueof a predetermined type, such as CIN 1 (cervical intraepithelialneoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades2 and/or 3), normal squamous, normal columnar, and metaplasia, forexample. Tissue-class probabilities are useful in characterizing thecondition (e.g., disease state, response to treatment, cell type, etc.)of a tissue.

[0010] The invention provides increased diagnostic sensitivity andspecificity by combining a plurality of statistical classificationtechniques to determine tissue-class probabilities for a tissue sample.Furthermore, in one embodiment, the invention comprises combining one ormore statistical classification techniques with one or morenon-statistical approaches in order to determine a condition of a tissuesample.

[0011] The invention provides increased diagnostic accuracy by applyingtwo or more statistical classification techniques to data from a regionof tissue. The two or more techniques may use different input data fromthe region. For example, reflectance data from a region corresponding toa first wavelength range may be used to determine a first set oftissue-class probabilities, while data corresponding to a secondwavelength range, different from the first, may be used to determine asecond set of tissue-class probabilities. Then, the invention comprisesdetermining a set of overall tissue-class probabilities based on thefirst and second sets of tissue-class probabilities.

[0012] In another embodiment, the two or more techniques differ in thatthey have different statistical bases. For example, one embodiment ofthe invention comprises determining a first set of tissue-classprobabilities by applying a statistical method based on maximal varianceof data between known classes, and determining a second set oftissue-class probabilities by applying a statistical method based onmaximal discrimination of data between known classes. Overalltissue-class probabilities are then computed using the two sets ofprobabilities resulting from the two statistical methods.

[0013] Tissue diagnostic information, especially relating to the diseasestate of the tissue, may not be determinable using only statisticalapproaches. For example, optical data obtained from a tissue sample mayindicate levels of substances—such as collagen, porphyrin, FAD, and/orNADH—which may be related to a tissue classification. In those cases,non-statistically-derived information may be taken into account byapplying a classification metric that is used with one or morestatistical classification schemes, as part of the overall processing ofdata. Accuracy may be increased further still by application of datamasking algorithms.

[0014] Data masking algorithms of the invention automatically identifydata from regions that are obstructed and regions that lie outside azone of interest of the tissue sample. Some of the masks of theinvention use spectral data, other masks use image data, and still othermasks use both spectral and image data from a region in order todetermine whether the region is obstructed and/or lies outside a zone ofinterest. A region of a tissue sample may be obstructed, for example, bymucus, fluid, foam, a portion of a speculum or other medical instrument,glare, shadow, and/or blood. Regions that lie outside a zone of interestinclude, for example, a vaginal wall, an os, a cervical edge, and tissuein the vicinity of a smoke tube. Generally, obstructed and outlierregions are those from which optical data are ambiguous or cannot beclassified.

[0015] The invention provides greater diagnostic accuracy byautomatically masking data that might otherwise result in erroneoustissue-class probabilities. For example, data from regions identified asobstructed or outside a zone of interest may be “hard masked”—that is,eliminated prior to computation of tissue-class probabilities. Theseregions may be characterized as having an indeterminate condition.

[0016] In some cases, data from regions that are only partiallyobstructed or which lie only partially outside a zone of interest arestill used to determine tissue-class probabilities. These probabilitiesmay be “soft masked”—that is, weighted according to a likelihood a pointwithin the region is affected by an obstruction and/or lies outside azone of interest.

[0017] Soft or hard masks may be applied in the present invention inorder to obtain a probability of a specific tissue condition. Forexample, processing of optical data in connection with the applicationof a necrosis mask may provide a probability that a specific region oftissue is necrotic. The masking parameters may be set such that theresult is binary (i.e., the tissue-class probability is either 0 or1.0). Thus, the result of masking may itself be an expression of atissue-class probability, and may encompass a data processing stepaccording to the invention.

[0018] In addition, the invention provides methods of obtaining andarbitrating between redundant sets of data obtained from a tissuesample. For example, one embodiment comprises obtaining two sets ofreflectance spectral data from the same region of a tissue sample,wherein each set is obtained using light incident to the region at adifferent angle. In this way, if one set of data is affected by anartifact, such as glare, shadow, or other obstruction, the other set ofdata provides a back-up that may not be affected by the artifact. Theinvention comprises methods of automatically determining whether one ormore data sets is/are affected by an artifact, and provides methods ofarbitrating between the multiple data sets in order to select arepresentative set of data for the region.

[0019] Accordingly, the invention comprises obtaining both spectral andimage data from one or more regions of a tissue sample, arbitratingbetween redundant data sets obtained from each region, automaticallymasking the data to identify regions that are outside a zone of interestor are affected by an obstruction, and processing the data using aplurality of statistical tissue classification techniques to determine,for each member of a set of predefined tissue classes, a probabilitythat the region comprises tissue within the predefined class. Methods ofthe invention also comprise evaluating a classification metric having anon-statistically-based component, and characterizing a condition of theregion according to either the classification metric (if satisfied) orthe set of tissue-class probabilities.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The objects and features of the invention can be betterunderstood with reference to the drawings described below, and theclaims. The drawings are not necessarily to scale, emphasis insteadgenerally being placed upon illustrating the principles of theinvention. In the drawings, like numerals are used to indicate likeparts throughout the various views. The patent or application filecontains at least one drawing executed in color. Copies of this patentor patent application publication with color drawing(s) will be providedby the U.S. Patent and Trademark Office upon request and payment of thenecessary fee.

[0021] While the invention is particularly shown and described hereinwith reference to specific examples and specific embodiments, it shouldbe understood by those skilled in the art that various changes in formand detail may be made therein without departing from the spirit andscope of the invention.

[0022]FIG. 1 is a block diagram featuring components of a tissuecharacterization system according to an illustrative embodiment of theinvention.

[0023]FIG. 2 is a schematic representation of components of theinstrument used in the tissue characterization system of FIG. 1 toobtain spectral data and image data from a tissue sample according to anillustrative embodiment of the invention.

[0024]FIG. 3 is a block diagram of the instrument used in the tissuecharacterization system of FIG. 1 according to an illustrativeembodiment of the invention.

[0025]FIG. 4 depicts a probe within a calibration port according to anillustrative embodiment of the invention.

[0026]FIG. 5 depicts an exemplary scan pattern used by the instrument ofFIG. 1 to obtain spatially-correlated spectral data and image data froma tissue sample according to an illustrative embodiment of theinvention.

[0027]FIG. 6 depicts front views of four exemplary arrangements ofillumination sources about a probe head according to variousillustrative embodiments of the invention.

[0028]FIG. 7 depicts exemplary illumination of a region of a tissuesample using light incident to the region at two different anglesaccording to an illustrative embodiment of the invention.

[0029]FIG. 8 depicts illumination of a cervical tissue sample using aprobe and a speculum according to an illustrative embodiment of theinvention.

[0030]FIG. 9 is a schematic representation of an accessory device for aprobe marked with identifying information in the form of a bar codeaccording to an illustrative embodiment of the invention.

[0031]FIG. 10 is a block diagram featuring spectral data calibration andcorrection components of the tissue characterization system of FIG. 1according to an illustrative embodiment of the invention.

[0032]FIG. 11 is a block diagram featuring the spectral datapre-processing component of the tissue characterization system of FIG. 1according to an illustrative embodiment of the invention.

[0033]FIG. 12 shows a graph depicting reflectance spectral intensity asa function of wavelength using an open air target according to anillustrative embodiment of the invention.

[0034]FIG. 13 shows a graph depicting reflectance spectral intensity asa function of wavelength using a null target according to anillustrative embodiment of the invention.

[0035]FIG. 14 shows a graph depicting fluorescence spectral intensity asa function of wavelength using an open air target according to anillustrative embodiment of the invention.

[0036]FIG. 15 shows a graph depicting fluorescence spectral intensity asa function of wavelength using a null target according to anillustrative embodiment of the invention.

[0037]FIG. 16 is a representation of regions of a scan pattern and showsvalues of 10 broadband reflectance intensity at each region using anopen air target according to an illustrative embodiment of theinvention.

[0038]FIG. 17 shows a graph depicting as a function of wavelength theratio of reflectance spectral intensity using an open air target to thereflectance spectral intensity using a null target according to anillustrative embodiment of the invention.

[0039]FIG. 18 shows a graph depicting as a function of wavelength theratio of fluorescence spectral intensity using an open air target to thefluorescence spectral intensity using a null target according to anillustrative embodiment of the invention.

[0040]FIG. 19 is a photograph of a customized target forfactory/preventive maintenance calibration and for pre-patientcalibration of the instrument used in the tissue characterization systemof FIG. 1 according to an illustrative embodiment of the invention.

[0041]FIG. 20 is a representation of the regions of the customizedtarget of FIG. 19 that are used to calibrate broadband reflectancespectral data according to an illustrative embodiment of the invention.

[0042]FIG. 21 shows a graph depicting as a function of wavelength themean reflectivity of the 10% diffuse target of FIG. 19 over thenon-masked regions shown in FIG. 20, measured using the same instrumenton two different days according to an illustrative embodiment of theinvention.

[0043]FIG. 22A shows a graph depicting, for various individualinstruments, curves of reflectance intensity (using the BB1 lightsource), each instrument curve representing a mean of reflectanceintensity values for regions confirmed as metaplasia by impression andfiltered according to an illustrative embodiment of the invention.

[0044]FIG. 22B shows a graph depicting, for various individualinstruments, curves of reflectance intensity of themetaplasia-by-impression regions of FIG. 22A, after adjustment accordingto an illustrative embodiment of the invention.

[0045]FIG. 23 shows a graph depicting the spectral irradiance of a NISTtraceable Quartz-Tungsten-Halogen lamp, along with a model of ablackbody emitter, used for determining an instrument responsecorrection for fluorescence intensity data according to an illustrativeembodiment of the invention.

[0046]FIG. 24 shows a graph depicting as a function of wavelength thefluorescence intensity of a dye solution at each region of a 499-pointscan pattern according to an illustrative embodiment of the invention.

[0047]FIG. 25 shows a graph depicting as a function of scan position thefluorescence intensity of a dye solution at a wavelength correspondingto a peak intensity seen in FIG. 24 according to an illustrativeembodiment of the invention.

[0048]FIG. 26 shows a graph depicting exemplary mean power spectra forvarious individual instruments subject to a noise performance criterionaccording to an illustrative embodiment of the invention.

[0049]FIG. 27A is a block diagram featuring steps an operator performsin relation to a patient scan using the system of FIG. 1 according to anillustrative embodiment of the invention.

[0050]FIG. 27B is a block diagram featuring steps that the system ofFIG. 1 performs during acquisition of spectral data in a patient scan todetect and compensate for movement of the sample during the scan.

[0051]FIG. 28 is a block diagram showing the architecture of a videosystem used in the system of FIG. 1 and how it relates to othercomponents of the system of FIG. 1 according to an illustrativeembodiment of the invention.

[0052]FIG. 29A is a single video image of a target of 10% diffusereflectivity upon which an arrangement of four laser spots is projectedin a target focus validation procedure according to an illustrativeembodiment of the invention.

[0053]FIG. 29B depicts the focusing image on the target in FIG. 29A withsuperimposed focus rings viewed by an operator through a viewfinderaccording to an illustrative embodiment of the invention.

[0054]FIG. 30 is a block diagram of a target focus validation procedureaccording to an illustrative embodiment of the invention.

[0055]FIG. 31 illustrates some of the steps of the target focusvalidation procedure of FIG. 30 as applied to the target in FIG. 29A.

[0056]FIG. 32A represents the green channel of an RGB image of acervical tissue sample, used in a target focus validation procedureaccording to an illustrative embodiment of the invention.

[0057]FIG. 32B represents an image of the final verified laser spots onthe cervical tissue sample of FIG. 32A, verified during application ofthe target focus validation procedure of FIG. 30 according to anillustrative embodiment of the invention.

[0058]FIG. 33 depicts a cervix model onto which laser spots areprojected during an exemplary application of the target focus validationprocedure of FIG. 30, where the cervix model is off-center such that theupper two laser spots fall within the os region of the cervix model,according to an illustrative embodiment of the invention.

[0059]FIG. 34 shows a graph depicting, as a function of probe position,the mean of a measure of focus of each of the four laser spots projectedonto the off-center cervix model of FIG. 33 in the target focusvalidation procedure of FIG. 30, according to an illustrative embodimentof the invention.

[0060]FIG. 35 shows a series of graphs depicting mean reflectancespectra for CIN 2/3 and non-CIN 2/3 tissues at a time prior toapplication of acetic acid, at a time corresponding to maximumwhitening, and at a time corresponding to the latest time at which datawas obtained—used in determining an optimal window for obtainingspectral data according to an illustrative embodiment of the invention.

[0061]FIG. 36 shows a graph depicting the reflectance discriminationfunction spectra useful for differentiating between CIN 2/3 and non-CIN2/3 tissues, used in determining an optimal window for obtainingspectral data according to an illustrative embodiment of the invention.

[0062]FIG. 37 shows a graph depicting the performance of two LDA (lineardiscriminant analysis) models as applied to reflectance data obtained atvarious times following application of acetic acid, used in determiningan optimal window for obtaining spectral data according to anillustrative embodiment of the invention.

[0063]FIG. 38 shows a series of graphs depicting mean fluorescencespectra for CIN 2/3 and non-CIN 2/3 tissues at a time prior toapplication of acetic acid, at a time corresponding to maximumwhitening, and at a time corresponding to the latest time at which datawas obtained, used in determining an optimal window for obtainingspectral data according to an illustrative embodiment of the invention.

[0064]FIG. 39 shows a graph depicting the fluorescence discriminationfunction spectra useful for differentiating between CIN 2/3 and non-CIN2/3 tissues in determining an optimal window for obtaining spectral dataaccording to an illustrative embodiment of the invention.

[0065]FIG. 40 shows a graph depicting the performance of two LDA (lineardiscriminant analysis) models as applied to fluorescence data obtainedat various times following application of acetic acid, used indetermining an optimal window for obtaining spectral data according toan illustrative embodiment of the invention.

[0066]FIG. 41 shows a graph depicting the performance of three LDAmodels as applied to data obtained at various times followingapplication of acetic acid, used in determining an optimal window forobtaining spectral data according to an illustrative embodiment of theinvention.

[0067]FIG. 42 shows a graph depicting the determination of an optimaltime window for obtaining diagnostic optical data using an opticalamplitude trigger, according to an illustrative embodiment of theinvention.

[0068]FIG. 43 shows a graph depicting the determination of an optimaltime window for obtaining diagnostic data using a rate of change of meanreflectance signal trigger, according to an illustrative embodiment ofthe invention.

[0069]FIG. 44A represents a 480×500 pixel image from a sequence ofimages of in vivo human cervix tissue and shows a 256×256 pixel portionof the image from which data is used in determining a correction for amisalignment between two images from a sequence of images of the tissuein the tissue characterization system of FIG. 1, according to anillustrative embodiment of the invention.

[0070]FIG. 44B depicts the image represented in FIG. 44A and shows a128×128 pixel portion of the image, made up of 16 individual 32×32 pixelvalidation cells, from which data is used in performing a validation ofthe misalignment correction determination according to an illustrativeembodiment of the invention.

[0071]FIG. 45 is a schematic flow diagram depicting steps in a method ofdetermining a correction for image misalignment in the tissuecharacterization system of FIG. 1, according to an illustrativeembodiment of the invention.

[0072]FIGS. 46A and 46B show a schematic flow diagram depicting steps ina version of the method shown in FIG. 45 of determining a correction forimage misalignment according to an illustrative embodiment of theinvention.

[0073]FIGS. 47A and 47B show a schematic flow diagram depicting steps ina version of the method shown in FIG. 45 of determining a correction forimage misalignment according to an illustrative embodiment of theinvention.

[0074] FIGS. 48A-F depict a subset of adjusted images from a sequence ofimages of a tissue with an overlay of gridlines showing the validationcells used in validating the determinations of misalignment correctionbetween the images according to an illustrative embodiment of theinvention.

[0075]FIG. 49A depicts a sample image after application of a 9-pixelsize (9×9) Laplacian of Gaussian filter (LoG 9 filter) on an exemplaryimage from a sequence of images of tissue, used in determining acorrection for image misalignment, according to an illustrativeembodiment of the invention.

[0076]FIG. 49B depicts the application of both a feathering techniqueand a Laplacian of Gaussian filter on the exemplary image used in FIG.49A to account for border processing effects, used in determining acorrection for image misalignment according to an illustrativeembodiment of the invention.

[0077]FIG. 50A depicts a sample image after application of a LoG 9filter on an exemplary image from a sequence of images of tissue, usedin determining a correction for image misalignment according to anillustrative embodiment of the invention.

[0078]FIG. 50B depicts the application of both a Hamming windowtechnique and a LoG 9 filter on the exemplary image in FIG. 50A toaccount for border processing effects in the determination of acorrection for image misalignment according to an illustrativeembodiment of the invention.

[0079] FIGS. 51A-F depict the determination of a correction for imagemisalignment using methods including the application of LoG filters ofvarious sizes, as well as the application of a Hamming window techniqueand a feathering technique according to illustrative embodiments of theinvention.

[0080]FIG. 52 shows a graph depicting exemplary mean values ofreflectance spectral data as a function of wavelength for tissue regionsaffected by glare, tissue regions affected by shadow, and tissue regionsaffected by neither glare nor shadow according to an illustrativeembodiment of the invention.

[0081]FIG. 53 shows a graph depicting mean values and standarddeviations of broadband reflectance spectral data using the BB1 channellight source for regions confirmed as being obscured by blood, obscuredby mucus, obscured by glare from the BB1 source, obscured by glare fromthe BB2 source, or unobscured, according to an illustrative embodimentof the invention.

[0082]FIG. 54 shows a graph depicting mean values and standarddeviations of broadband reflectance spectral data using the BB2 channellight source for regions confirmed as being obscured by blood, obscuredby mucus, obscured by glare from the BB1 source, obscured by glare fromthe BB2 source, or unobscured, according to an illustrative embodimentof the invention.

[0083]FIG. 55 shows a graph depicting the weighted difference betweenthe mean reflectance values of glare-obscured regions and unobscuredregions of tissue as a function of wavelength used in determiningmetrics for application in the arbitration step in FIG. 1, according toan illustrative embodiment of the invention.

[0084]FIG. 56 shows a graph depicting the weighted difference betweenthe mean reflectance values of blood-obscured regions and unobscuredregions of tissue as a function of wavelength used in determiningmetrics for application in the arbitration step in FIG. 1, according toan illustrative embodiment of the invention.

[0085]FIG. 57 shows a graph depicting the weighted difference betweenthe mean reflectance values of mucus-obscured regions and unobscuredregions of tissue as a function of wavelength, used in determiningmetrics for application in the arbitration step in FIG. 1 according toan illustrative embodiment of the invention.

[0086]FIG. 58 shows a graph depicting a ratio of the weighteddifferences between the mean reflectance values of glare-obscuredregions and unobscured-regions of tissue at two wavelengths, used indetermining metrics for application in the arbitration step in FIG. 1according to an illustrative embodiment of the invention.

[0087]FIG. 59 shows a graph depicting a ratio of the weighteddifferences between the mean reflectance values of blood-obscuredregions and unobscured regions of tissue at two wavelengths, used indetermining metrics for application in the arbitration step in FIG. 1according to an illustrative embodiment of the invention.

[0088]FIG. 60 shows a graph depicting a ratio of the weighteddifferences between the mean reflectance values of mucus-obscuredregions and unobscured regions of tissue at two wavelengths, used indetermining metrics for application in the arbitration step in FIG. 1according to an illustrative embodiment of the invention.

[0089]FIG. 61 shows a graph depicting as a function of wavelength meanvalues and confidence intervals of a ratio of BB1 and BB2 broadbandreflectance spectral values for regions confirmed as being eitherglare-obscured or shadow-obscured tissue, used in determining metricsfor application in the arbitration step in FIG. 1 according to anillustrative embodiment of the invention.

[0090]FIG. 62 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue where the BB1 data is affected byglare but the BB2 data is not, according to an illustrative embodimentof the invention.

[0091]FIG. 63 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue where the BB2 data is affected byshadow but the BB1 data is not, according to an illustrative embodimentof the invention.

[0092]FIG. 64 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue that is obscured by blood,according to an illustrative embodiment of the invention.

[0093]FIG. 65 shows a graph depicting BB1 and BB2 broadband reflectancespectral data for a region of tissue that is unobscured, according to anillustrative embodiment of the invention.

[0094]FIG. 66 shows a graph depicting the reduction in the variabilityof broadband reflectance measurements of CIN 2/3-confirmed tissueproduced by applying the metrics in the arbitration step 128 of FIG. 1to remove data affected by an artifact, according to an illustrativeembodiment of the invention.

[0095]FIG. 67 shows a graph depicting the reduction in the variabilityof broadband reflectance measurements of tissue classified as “noevidence of disease confirmed by pathology” produced by applying themetrics in the arbitration step 128 of FIG. 1 to remove data affected byan artifact, according to an illustrative embodiment of the invention.

[0096]FIG. 68 shows a graph depicting the reduction in the variabilityof broadband reflectance measurements of tissue classified as“metaplasia by impression” produced by applying the metrics in thearbitration step 128 of FIG. 1 to remove data affected by an artifact,according to an illustrative embodiment of the invention.

[0097]FIG. 69 shows a graph depicting the reduction in the variabilityof broadband reflectance measurements of tissue classified as “normal byimpression” produced by applying the metrics in the arbitration step 128of FIG. 1 to remove data affected by an artifact, according to anillustrative embodiment of the invention.

[0098]FIG. 70A depicts an exemplary image of cervical tissue dividedinto regions for which two types of reflectance spectral data and onetype of fluorescence spectral data are obtained, according to anillustrative embodiment of the invention.

[0099]FIG. 70B is a representation of the regions depicted in FIG. 70Aand shows the categorization of each region using the metrics in thearbitration step 128 of FIG. 1, according to an illustrative embodimentof the invention.

[0100]FIG. 71A depicts an exemplary image of cervical tissue dividedinto regions for which two types of reflectance spectral data and onetype of fluorescence spectral data are obtained, according to anillustrative embodiment of the invention.

[0101]FIG. 71B is a representation of the regions depicted in FIG. 71Aand shows the categorization of each region using the metrics in thearbitration step 128 of FIG. 1, according to an illustrative embodimentof the invention.

[0102]FIG. 72A depicts an exemplary image of cervical tissue dividedinto regions for which two types of reflectance spectral data and onetype of fluorescence spectral data are obtained, according to anillustrative embodiment of the invention.

[0103]FIG. 72B is a representation of the regions depicted in FIG. 72Aand shows the categorization of each region using the metrics in thearbitration step 128 of FIG. 1, according to an illustrative embodimentof the invention.

[0104]FIG. 73 is a block diagram depicting steps in a method ofprocessing and combining spectral data and image data obtained in thetissue characterization system of FIG. 1 to determine states of healthof regions of a tissue sample, according to an illustrative embodimentof the invention.

[0105]FIG. 74 is a block diagram depicting steps in the method of FIG.73 in further detail, according to an illustrative embodiment of theinvention.

[0106]FIG. 75 shows a scatter plot depicting discrimination betweenregions of normal squamous tissue and CIN 2/3 tissue for known referencedata, obtained by comparing fluorescence intensity at about 460 nm to aratio of fluorescence intensities at about 505 nm and about 410 nm, usedin determining an NED spectral mask (NED_(spec)) according to anillustrative embodiment of the invention.

[0107]FIG. 76 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known normal squamous tissue regionsand known CIN 2/3 tissue regions, used in determining an NED spectralmask (NED_(spec)) according to an illustrative embodiment of theinvention.

[0108]FIG. 77 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known squamous tissue regions andknown CIN 2/3 tissue regions, used in determining an NED spectral mask(NED_(spec)) according to an illustrative embodiment of the invention.

[0109]FIG. 78 shows a graph depicting values of a discriminationfunction using a range of numerator wavelengths and denominatorwavelengths in the discrimination analysis between known normal squamoustissue regions and known CIN 2/3 tissue regions, used in determining anNED spectral mask (NED_(spec)) according to an illustrative embodimentof the invention.

[0110]FIG. 79A depicts an exemplary reference image of cervical tissuefrom a patient scan in which spectral data is used in arbitration, NEDspectral masking, and statistical classification of interrogation pointsof the tissue sample, according to an illustrative embodiment of theinvention.

[0111]FIG. 79B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 79A and shows pointsclassified as “filtered” following arbitration, “masked” following NEDspectral masking with two different sets of parameters, and “CIN 2/3”following statistical classification, according to an illustrativeembodiment of the invention.

[0112]FIG. 79C is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 79A and shows pointsclassified as “filtered” following arbitration, “masked” following NEDspectral masking with two different sets of parameters, and “CIN 2/3”following statistical classification, according to an illustrativeembodiment of the invention.

[0113]FIG. 79D is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 79A and shows pointsclassified as “filtered” following arbitration, “masked” following NEDspectral masking with two different sets of parameters, and “CIN 2/3”following statistical classification, according to an illustrativeembodiment of the invention.

[0114]FIG. 80 shows a graph depicting fluorescence intensity as afunction of wavelength from an interrogation point confirmed as invasivecarcinoma by pathology and necrotic tissue by impression, used indetermining a Necrosis spectral mask according to an illustrativeembodiment of the invention.

[0115]FIG. 81 shows a graph depicting broadband reflectance BB1 and BB2as functions of wavelength from an interrogation point confirmed asinvasive carcinoma by pathology and necrotic tissue by impression, usedin determining a Necrosis spectral mask according to an illustrativeembodiment of the invention.

[0116]FIG. 82A depicts an exemplary reference image of cervical tissuefrom the scan of a patient confirmed as having advanced invasive cancerin which spectral data is used in arbitration, Necrosis spectralmasking, and statistical classification of interrogation points of thetissue sample, according to an illustrative embodiment of the invention.

[0117]FIG. 82B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 82A and shows pointsclassified as “filtered” following arbitration, “masked” followingapplication of the “Porphyrin” and “FAD” portions of the Necrosisspectral mask, and “CIN 2/3” following statistical classification,according to an illustrative embodiment of the invention.

[0118]FIG. 83 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known cervical edge regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

[0119]FIG. 84 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known cervical edge regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

[0120]FIG. 85 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known vaginal wall regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

[0121]FIG. 86 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known vaginal wall regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine acervical edge/vaginal wall ([CE]_(spec)) spectral mask according to anillustrative embodiment of the invention.

[0122]FIG. 87A depicts an exemplary reference image of cervical tissuefrom a patient scan in which spectral data is used in arbitration andcervical edge/vaginal wall ([CE]_(spec)) spectral masking, according toan illustrative embodiment of the invention.

[0123]FIG. 87B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 87A and shows pointsclassified as “filtered” following arbitration and “masked” followingcervical edge/vaginal wall ([CE]_(spec)) spectral masking, according toan illustrative embodiment of the invention.

[0124]FIG. 88 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known pooling fluids regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

[0125]FIG. 89 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known pooling fluids regions and knownCIN 2/3 tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

[0126]FIG. 90 shows a graph depicting as a function of wavelength meanbroadband reflectance values for known mucus regions and known CIN 2/3tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

[0127]FIG. 91 shows a graph depicting as a function of wavelength meanfluorescence intensity values for known mucus regions and known CIN 2/3tissue regions, used in a discrimination analysis to determine afluids/mucus ([MU]_(spec)) spectral mask according to an illustrativeembodiment of the invention.

[0128]FIG. 92A depicts an exemplary reference image of cervical tissuefrom a patient scan in which spectral data is used in arbitration andfluids/mucus ([MU]_(spec)) spectral masking, according to anillustrative embodiment of the invention.

[0129]FIG. 92B is a representation (obgram) of the interrogation points(regions) of the tissue sample depicted in FIG. 92A and shows pointsclassified as “filtered” following arbitration and “masked” followingfluids/mucus ([MU]_(spec)) spectral masking, according to anillustrative embodiment of the invention.

[0130]FIG. 93 depicts image masks determined from an image of a tissuesample and shows how the image masks are combined with respect to eachspectral interrogation point (region) of the tissue sample, according toan illustrative embodiment of the invention.

[0131]FIG. 94A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a correspondingglare image mask, Glare_(vid), according to an illustrative embodimentof the invention.

[0132]FIG. 94B represents a glare image mask, Glare_(vid), correspondingto the exemplary image in FIG. 94A, according to an illustrativeembodiment of the invention.

[0133]FIG. 95 is a block diagram depicting steps in a method ofdetermining a glare image mask, Glare_(vid), for an image of cervicaltissue, according to an illustrative embodiment of the invention.

[0134]FIG. 96 shows a detail of a histogram used in a method ofdetermining a glare image mask, Glare_(vid), for an image of cervicaltissue, according to an illustrative embodiment of the invention.

[0135]FIG. 97A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a correspondingregion-of-interest image mask, [ROI]_(vid), according to an illustrativeembodiment of the invention.

[0136]FIG. 97B represents a region-of-interest image mask, [ROI]vid,corresponding to the exemplary image in FIG. 120A, according to anillustrative embodiment of the invention.

[0137]FIG. 98 is a block diagram depicting steps in a method ofdetermining a region-of-interest image mask, [ROI]_(vid), for an imageof cervical tissue, according to an illustrative embodiment of theinvention.

[0138]FIG. 99A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a correspondingsmoke tube image mask, [ST]_(vid), according to an illustrativeembodiment of the invention.

[0139]FIG. 99B represents a smoke tube image mask, [ST]_(vid),corresponding to the exemplary image in FIG. 99A, according to anillustrative embodiment of the invention.

[0140]FIG. 100 is a block diagram depicting steps in a method ofdetermining a smoke tube image mask, [ST]_(vid), for an image ofcervical tissue, according to an illustrative embodiment of theinvention.

[0141]FIG. 101A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a corresponding osimage mask, OS_(vid), according to an illustrative embodiment of theinvention.

[0142]FIG. 101B represents an os image mask, OS_(vid), corresponding tothe exemplary image in FIG. 101A, according to an illustrativeembodiment of the invention.

[0143]FIG. 102 is a block diagram depicting steps in a method ofdetermining an os image mask, OS_(vid), for an image of cervical tissue,according to an illustrative embodiment of the invention.

[0144]FIG. 103A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a correspondingblood image mask, Blood_(vid), according to an illustrative embodimentof the invention.

[0145]FIG. 103B represents a blood image mask, Blood_(vid),corresponding to the exemplary image in FIG. 103A, according to anillustrative embodiment of the invention.

[0146]FIG. 104 is a block diagram depicting steps in a method ofdetermining a blood image mask, Blood_(vid), for an image of cervicaltissue, according to an illustrative embodiment of the invention.

[0147]FIG. 105A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a correspondingmucus image mask, Mucus_(vid), according to an illustrative embodimentof the invention.

[0148]FIG. 105B represents a mucus image mask, Mucus_(vid),corresponding to the exemplary reference image in FIG. 105A, accordingto an illustrative embodiment of the invention.

[0149]FIG. 106 is a block diagram depicting steps in a method ofdetermining a mucus image mask, Mucus_(vid), for an image of cervicaltissue, according to an illustrative embodiment of the invention.

[0150]FIG. 107A depicts an exemplary reference image of cervical tissueobtained during a patient examination and used in determining acorresponding speculum image mask, [SP]_(vid), according to anillustrative embodiment of the invention.

[0151]FIG. 107B represents a speculum image mask, [SP]_(vid),corresponding to the exemplary image in FIG. 107A, according to anillustrative embodiment of the invention.

[0152]FIG. 108 is a block diagram depicting steps in a method ofdetermining a speculum image mask, [SP]_(vid), for an image of cervicaltissue, according to an illustrative embodiment of the invention.

[0153]FIG. 109A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a vaginal wallimage mask, [VW]_(vid), according to an illustrative embodiment of theinvention.

[0154]FIG. 109B represents the image of FIG. 109A overlaid with avaginal wall image mask, [VW]_(vid), following extension, determinedaccording to an illustrative embodiment of the invention.

[0155]FIG. 110 is a block diagram depicting steps in a method ofdetermining a vaginal wall image mask, [VW]_(vid), for an image ofcervical tissue, according to an illustrative embodiment of theinvention.

[0156]FIG. 111A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used in determining a correspondingfluid-and-foam image mask, [FL]_(vid), according to an illustrativeembodiment of the invention.

[0157]FIG. 111B represents a fluid-and-foam image mask, [FL]_(vid),corresponding to the exemplary image in FIG. 111A, according to anillustrative embodiment of the invention.

[0158]FIG. 112 is a block diagram depicting steps in a method ofdetermining a fluid-and-foam image mask, [FL]_(vid), for an image ofcervical tissue, according to an illustrative embodiment of theinvention.

[0159] FIGS. 113A-C show graphs representing a step in a method of imagevisual enhancement in which a piecewise linear transformation of aninput image produces an output image with enhanced image brightness andcontrast, according to one embodiment of the invention.

[0160]FIG. 114A depicts an exemplary image of cervical tissue obtainedduring a patient examination and used as a reference (base) image in amethod of disease probability display, according to one embodiment ofthe invention.

[0161]FIG. 114B depicts the output overlay image corresponding to thereference image in FIG. 114A, produced using a method of diseaseprobability display according to one embodiment of the invention.

[0162]FIG. 115A represents a disease display layer produced in a methodof disease probability display for the reference image in FIG. 114A,wherein CIN 2/3 probabilities at interrogation points are represented bycircles with intensities scaled by CIN 2/3 probability, according to oneembodiment of the invention.

[0163]FIG. 115B represents the disease display layer of FIG. 114Bfollowing filtering using a Hamming filter, according to one embodimentof the invention.

[0164]FIG. 116 represents the color transformation used to determine thedisease display layer image in a disease probability display method,according to one embodiment of the invention.

[0165]FIG. 117A depicts an exemplary reference image of cervical tissuehaving necrotic regions, obtained during a patient examination and usedas a reference (base) image in a method of disease probability display,according to one embodiment of the invention.

[0166]FIG. 117B depicts the output overlay image corresponding to thereference image in FIG. 117A, including necrotic regions, indeterminateregions, and CIN 2/3 regions, and produced using a method of diseaseprobability display according to one embodiment of the invention.

DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENT

[0167] Table of Contents Page System overview 32 Instrument 38 Spectralcalibration 51 Patient scan procedure 99 Video calibration and focusing102 Determining optimal data acquisition window 114 Motion tracking 131Broadband reflectance arbitration and low-signal masking 158Classification system overview 180 Spectral masking 186 Image masking197 Glare_(vid) 203 [ROI]_(vid) 208 [ST]_(vid) 209 Os_(vid) 217Blood_(vid) 222 Mucus_(vid) 226 [SP]_(vid) 231 [VW]_(vid) 242 [FL]_(vid)256 Classifiers 265 Combining spectral and image data 276 Imageenhancement 285 Diagnostic display 291

[0168] The Table of Contents above is provided as a generalorganizational guide to the Description of the Illustrative Embodiment.Entries in the Table do not serve to limit support for any given elementof the invention to a particular section of the Description.

System 100 Overview

[0169] The invention provides systems and methods for obtaining spectraldata and image data from a tissue sample, for processing the data, andfor using the data to diagnose the tissue sample. As used herein,“spectral data” from a tissue sample includes data corresponding to anywavelength of the electromagnetic spectrum, not just the visiblespectrum. Where exact wavelengths are specified, alternate embodimentscomprise using wavelengths within a ±5 m range of the given value,within a 10 nm range of the given value, and within a 125 nm range ofthe given value. As used herein, “image data” from a tissue sampleincludes data from a visual representation, such as a photo, a videoframe, streaming video, and/or an electronic, digital or mathematicalanalogue of a photo, video frame, or streaming video. As used herein, a“tissue sample” may comprise, for example, animal tissue, human tissue,living tissue, and/or dead tissue. A tissue sample may be in vivo, insitu, ex vivo, or ex situ, for example. A tissue sample may comprisematerial in the vacinity of tissue, such as non-biological materialsincluding dressings, chemical agents, and/or medical instruments, forexample.

[0170] Embodiments of the invention include obtaining data from a tissuesample, determining which data are of diagnostic value, processing theuseful data to obtain a prediction of disease state, and displaying theresults in a meaningful way. In one embodiment, spectral data and imagedata are obtained from a tissue sample and are used to create adiagnostic map of the tissue sample showing regions in which there is ahigh probability of disease.

[0171] The systems and methods of the invention can be used to performan examination of in situ tissue without the need for excision orbiopsy. In an illustrative embodiment, the systems and methods are usedto perform in-situ examination of the cervical tissue of a patient in anon-surgical setting, such as in a doctor's office or examination room.The examination may be preceded or accompanied by a routine pap smearand/or colposcopic examination, and may be followed-up by treatment orbiopsy of suspect tissue regions.

[0172]FIG. 1 depicts a block diagram featuring components of a tissuecharacterization system 100 according to an illustrative embodiment ofthe invention. Each component of the system 100 is discussed in moredetail herein. The system includes components for acquiring data,processing data, calculating disease probabilities, and displayingresults.

[0173] In the illustrative system 100 of FIG. 1, an instrument 102obtains spectral data and image data from a tissue sample. Theinstrument 102 obtains spectral data from each of a plurality of regionsof the sample during a spectroscopic scan of the tissue 104. During ascan, video images of the tissue are also obtained by the instrument102. Illustratively, one or more complete spectroscopic spectra areobtained for each of 500 discrete regions of a tissue sample during ascan lasting about 12 seconds. However, in other illustrativeembodiments any number of discrete regions may be scanned and theduration of each scan may vary. Since in-situ tissue may shift due toinvoluntary or voluntary patient movement during a scan, video imagesare used to detect shifts of the tissue, and to account for the shiftsin the diagnostic analysis of the tissue. Preferably, a detected shiftis compensated for in real time 106. For example, as described below infurther detail, one or more components of the instrument 102 may beautomatically adjusted during the examination of a patient whilespectral data are obtained in order to compensate for a detected shiftcaused by patient movement. Additionally or alternatively, the real-timetracker 106 provides a correction for patient movement that is used toprocess the spectral data before calculating disease probabilities. Inaddition to using image data to track movement, the illustrative system100 of FIG. 1 uses image data to identify regions that are obstructed orare outside the areas of interest of a tissue sample 108. This featureof the system 100 of FIG. 1 is discussed herein in more detail.

[0174] The system 100 shown in FIG. 1 includes components for performingfactory tests and periodic preventive maintenance procedures 110, theresults of which 112 are used to preprocess patient spectral data 114.In addition, reference spectral calibration data are obtained 116 in anexamination setting prior to each patient examination, and the results118 of the pre-patient calibration are used along with the factory andpreventive maintenance results 112 to preprocess patient spectral data114.

[0175] The instrument 102 of FIG. 1 includes a frame grabber 120 forobtaining a video image of the tissue sample. A focusing method 122 isapplied and video calibration is performed 124. The corrected video datamay then be used to compensate for patient movement during thespectroscopic data acquisition 104. The corrected video data is alsoused in image masking 108, which includes identifying obstructed regionsof the tissue sample, as well as regions of tissue that lie outside anarea of diagnostic interest. In one illustrative embodiment, during apatient scan, a single image is used to compute image masks 108 and todetermine a brightness and contrast correction 126 for displayingdiagnostic results. In illustrative alternative embodiments, more thanone image is used to create image masks and/or to determine a visualdisplay correction.

[0176] In the system of FIG. 1, spectral data are acquired 104 within apredetermined period of time following the application of a contrastagent, such as acetic acid, to the tissue sample. According to theillustrative embodiment, four raw spectra are obtained for each ofapproximately 500 regions of the tissue sample and are processed. Afluorescence spectrum, two broadband reflectance (backscatter) spectra,and a reference spectrum are obtained at each of the regions over arange from about 360 nm to about 720 nm wavelength. The period of timewithin which a scan is acquired is chosen so that the accuracy of theresulting diagnosis is maximized. In one illustrative embodiment, aspectral data scan of a cervical tissue sample is performed over anapproximately 12-second period of time within a range between about 30seconds and about 130 seconds following application of acetic acid tothe tissue sample.

[0177] The illustrative system 100 includes data processing componentsfor identifying data that are potentially non-representative of thetissue sample. Preferably, potentially non-representative data areeither hard-masked or soft-masked. Hard-masking of data includeseliminating the identified, potentially non-representative data fromfurther consideration. This results in an indeterminate diagnosis in thecorresponding region. Hard masks are determined in components 128, 130,and 108 of the system 100. Soft masking includes applying a weightingfunction or weighting factor to the identified, potentiallynon-representative data. The weighting is taken into account duringcalculation of disease probability 132, and may or may not result in anindeterminate diagnosis in the corresponding region. Soft masks aredetermined in component 130 of the system 100.

[0178] Soft masking provides a means of weighting spectral dataaccording to the likelihood that the data is representative of clear,unobstructed tissue in a region of interest. For example, if the system100 determines there is a possibility that one kind of data from a givenregion is affected by an obstruction, such as blood or mucus, that datais “penalized” by attributing a reduced weighting to that data duringcalculation of disease probability 132. Another kind of data from thesame region that is determined by the system 100 not to be affected bythe obstruction is more heavily weighted in the diagnostic step than thepossibly-affected data, since the unaffected data is attributed agreater weighting in the calculation of disease probability 132.

[0179] In the illustrative system 100, soft masking is performed inaddition to arbitration of two or more redundant data sets. Arbitrationof data sets is performed in component 128. In the illustrativeembodiment, this type of arbitration employs the following steps:obtaining two sets of broadband reflectance (backscatter) data from eachregion of the tissue sample using light incident to the region at twodifferent angles; determining if one of the data sets is affected by anartifact such as shadow, glare, or obstruction; eliminating one of theredundant reflectance data sets so affected; and using the other dataset in the diagnosis of the tissue at the region. If both of the datasets are unaffected by an artifact, a mean of the two sets is used.

[0180] According to the illustrative embodiment, the instrument 102obtains both video images and spectral data from a tissue sample. Thespectral data may include fluorescence data and broadband reflectance(backscatter) data. The raw spectral data are processed and then used ina diagnostic algorithm to determine disease probability for regions ofthe tissue sample. According to the illustrative embodiment, both imagedata and spectral data are used to mask data that is potentiallynon-representative of unobstructed regions of interest of the tissue. Inanother illustrative embodiment, both the image data and the spectraldata are alternatively or additionally used in the diagnostic algorithm.

[0181] The system 100 also includes a component 132 for determining adisease probability at each of a plurality of the approximately 500interrogation points using spectral data processed in the components 128and 130 and using the image masks determined in component 108.Illustratively, the disease probability component 132 processes spectraldata with statistical and/or heuristics-based(non-statistically-derived) spectral classifiers 134, incorporates imageand/or spectral mask information 136, and assigns a probability of highgrade disease, such as CIN 2+, to each examined region of the tissuesample. The classifiers use stored, accumulated training data fromsamples of known disease state. The disease display component 138graphically presents regions of the tissue sample having the highestprobability of high grade disease by employing a color map overlay ofthe cervical tissue sample. The disease display component 138 alsodisplays regions of the tissue that are necrotic and/or regions at whicha disease probability could not be determined.

[0182] Each of the components of the illustrative system 100 isdescribed in more detail below.

Instrument—102

[0183]FIG. 2 is a schematic representation of components of theinstrument 102 used in the tissue characterization system 100 of FIG. 1to obtain spectral data and image data from a tissue sample according toan illustrative embodiment of the invention. The instrument of FIG. 2includes a console 140 connected to a probe 142 by way of a cable 144.The cable 144 carries electrical and optical signals between the console140 and the probe 142. In an alternative embodiment, signals aretransmitted between the console 140 and the probe 142 wirelessly,obviating the need for the cable 144. The probe 142 accommodates adisposable component 146 that comes into contact with tissue and may bediscarded after one use. The console 140 and the probe 142 aremechanically connected by an articulating arm 148, which can alsosupport the cable 144. The console 140 contains much of the hardware andthe software of the system, and the probe 142 contains the necessaryhardware for making suitable spectroscopic observations. The details ofthe instrument 100 are further explained in conjunction with FIG. 3.

[0184]FIG. 3 shows an exemplary operational block diagram 150 of aninstrument 102 of the type depicted in FIG. 2. Referring to FIGS. 1 and2, in some illustrative embodiments the instrument 102 includes featuresof single-beam spectrometer devices, but is adapted to include otherfeatures of the invention. In other illustrative embodiments, theinstrument 102 is substantially the same as double-beam spectrometerdevices, adapted to include other features of the invention. In stillother illustrative embodiments the instrument 102 employs other types ofspectroscopic devices. In the depicted embodiment, the console 140includes a computer 152, which executes software that controls theoperation of the instrument 102. The software includes one or moremodules recorded on machine-readable media such as magnetic disks,magnetic tape, CD-ROM, and semiconductor memory, for example.Preferably, the machine-readable medium is resident within the computer152. In alternative embodiments, the machine-readable medium can beconnected to the computer 152 by a communication link. However, inalternative embodiments, one can substitute computer instructions in theform of hardwired logic for software, or one can substitute firmware(i.e., computer instructions recorded on devices such as PROMS, EPROMS,EEPROMs, or the like) for software. The term machine-readableinstructions as used herein is intended to encompass software, hardwiredlogic, firmware, object code and the like.

[0185] The computer 152 of the instrument 102 is preferably a generalpurpose computer. The computer 152 can be, for example, an embeddedcomputer, a personal computer such as a laptop or desktop computer, oranother type of computer, that is capable of running the software,issuing suitable control commands, and recording information inreal-time. The illustrative computer 152 includes a display 154 forreporting information to an operator of the instrument 102, a keyboard156 for enabling the operator to enter information and commands, and aprinter 158 for providing a print-out, or permanent record, ofmeasurements made by the instrument 102 and for printing diagnosticresults, for example, for inclusion in the chart of a patient. Accordingto the illustrative embodiment of the invention, some commands enteredat the keyboard 156 enable a user to perform certain data processingtasks, such as selecting a particular spectrum for analysis, rejecting aspectrum, and/or selecting particular segments of a spectrum fornormalization. Other commands enable a user to select the wavelengthrange for each particular segment and/or to specify both wavelengthcontiguous and non-contiguous segments. In one illustrative embodiment,data acquisition and data processing are automated and require little orno user input after initializing a scan.

[0186] The illustrative console 140 also includes an ultraviolet (UV)source 160 such as a nitrogen laser or a frequency-tripled Nd:YAG laser,one or more white light sources 162 such as one, two, three, four, ormore Xenon flash lamps, and control electronics 164 for controlling thelight sources both as to intensity and as to the time of onset ofoperation and the duration of operation. One or more power supplies 166are included in the illustrative console 140 to provide regulated powerfor the operation of all of the components of the instrument 102. Theillustrative console 140 of FIG. 3 also includes at least onespectrometer and at least one detector (spectrometer and detector 168)suitable for use with each of the light sources. In some illustrativeembodiments, a single spectrometer operates with both the UV lightsource 160 and the white light source(s) 162. The same detector mayrecord both UV and white light signals. However, in other illustrativeembodiments, different detectors are used for each light source.

[0187] The illustrative console 140 further includes coupling optics 170to couple the UV illumination from the UV light source 160 to one ormore optical fibers in the cable 144 for transmission to the probe 142,and coupling optics 172 for coupling the white light illumination fromthe white light source(s) 162 to one or more optical fibers in the cable144 for transmission to the probe 142. The spectral response of aspecimen to UV illumination from the UV light source 160 observed by theprobe 142 is carried by one or more optical fibers in the cable 144 fortransmission to the spectrometer and detector 168 in the console 140.The spectral response of a specimen to the white light illumination fromthe white light source(s) 162 observed by the probe 142 is carried byone or more optical fibers in the cable 144 for transmission to thespectrometer and detector 168 in the console 140. As shown in FIG. 3,the console 140 includes a footswitch 174 to enable an operator of theinstrument 102 to signal when it is appropriate to commence a spectralscan by stepping on the switch. In this manner, the operator has his orher hands free to perform other tasks, for example, aligning the probe142.

[0188] The console 140 additionally includes a calibration port 176 intowhich a calibration target may be placed for calibrating the opticalcomponents of the instrument 102. Illustratively, an operator places theprobe 142 in registry with the calibration port 176 and issues a commandthat starts the calibration operation. In illustrative calibrationoperation, a calibrated light source provides a calibration signal inthe form of an illumination of known intensity over a range ofwavelengths, and/or at a number of discrete wavelengths. The probe 142detects the calibration signal, and transmits the detected signalthrough the optical fiber in the cable 144 to the spectrometer anddetector 168. A test spectral result is obtained. A calibration of thespectral system can be computed as the ratio of the amplitude of theknown illumination at a particular wavelength divided by the testspectral result at the same wavelength. Calibration may include factorycalibration 110, preventive maintenance calibration 110, and/orpre-patient calibration 116, as shown in the system 100 of FIG. 1.Pre-patient calibration 116 may be performed to account forpatient-to-patient variation, for example.

[0189]FIG. 4 depicts the illustrative probe 142 of FIG. 2 resting withina calibration port 176 according to an illustrative embodiment of theinvention. Referring to FIGS. 2-4, the illustrative calibration port 176is adjustably attached to the probe 142 or the console 140 to allow anoperator to perform pre-patient calibration without assemblingdetachable parts. The pre-patient calibration port may contain one ormore pre-positioned calibration targets, such as a customized target 426(see also FIG. 19) and a null target 187, both described in more detailbelow.

[0190] According to the illustrative embodiment, factory and/orpreventive maintenance calibration includes using a portable, detachablecalibration port to calibrate any number of individual units, allowingfor a standardized calibration procedure among various instruments.Preferably, the calibration port 176 is designed to prevent stray roomlight or other external light from affecting a calibration measurementwhen a calibration target is in place in the calibration port 176. Forexample, as shown in FIG. 104, the null target 187 can be positioned upagainst the probe head 192 by way of an actuator 189 such that theeffect of external stray light is minimized. When not in use, the nulltarget 187 is positioned out of the path of light between the customizedtarget 426 and the collection optics 200, as depicted in FIG. 4. Anadditional fitting may be placed over the probe head 192 to furtherreduce the effect of external stray light. According to one illustrativeembodiment, the target 187 in the calibration port 176 is locatedapproximately 100 mm from the probe head 192; and the distance lighttravels from the target 187 to the first optical component of the probe142 is approximately 130 mm. The location of the target (in relation tothe probe head 192) during calibration may approximate the location oftissue during a patient scan.

[0191] The illustrative probe 142 includes probe optics 178 forilluminating a specimen to be analyzed with UV light from the UV source160 and for collecting the fluorescent and broadband reflectance(backscatter) illumination from the specimen being analyzed. Theillustrative probe 142 of FIGS. 2 and 3 includes a scanner assembly 180that provides illumination from the UV source 160, for example, in araster pattern over a target area of the specimen of cervical tissue tobe analyzed. The probe 142 also includes a video camera 182 forobserving and recording visual images of the specimen under analysis.The probe 142 also includes a targeting source 184 for determining whereon the surface of the specimen to be analyzed the probe 142 is pointing.The probe 142 also includes white light optics 186 to deliver whitelight from the white light source(s) 162 for recording the reflectancedata and to assist the operator in visualizing the specimen to beanalyzed. Once the operator aligns the instrument 102 and depresses thefootswitch 174, the computer 152 controls the actions of the lightsources 160, 162, the coupling optics 170, 172, the transmission oflight signals and electrical signals through the cable 144, theoperation of the probe optics 178 and the scanner assembly 180, theretrieval of observed spectra, the coupling of the observed spectra intothe spectrometer and detector 168 via the cable 144, the operation ofthe spectrometer and detector 168, and the subsequent signal processingand analysis of the recorded spectra.

[0192]FIG. 4 depicts the probe 142 having top and bottom illuminationsources 188, 190 according to an illustrative embodiment of theinvention. In this embodiment, the illumination sources 188, 190 aresituated at an upper and a lower location about the perimeter of a probehead 192 such that there is illuminating light incident to a target areaat each of two different angles. In one embodiment, the target area is atissue sample. The probe head 192 contains probe optics 178 forilluminating regions of tissue and for collecting illumination reflectedor otherwise emitted from regions of tissue. Illustratively, the probeoptics for collecting the illumination 200 are located between the topand bottom illumination sources 188, 190. In other illustrativeembodiments, other arrangements of the illuminating and collecting probeoptics 178 are used that allow the illumination of a given region oftissue with light incident to the region at more than one angle. Onesuch arrangement includes the collecting optics 200 positioned aroundthe illuminating optics.

[0193] In one illustrative embodiment, the top and bottom illuminationsources 188, 190 are alternately turned on and off in order tosequentially illuminate the tissue at equal and opposite angles relativeto the collection axis. For example, the top illumination source 188 isturned on while the bottom illumination source 190 is turned off, suchthat spectral measurements may be obtained for light reflected from aregion of the tissue sample 194 illuminated with light incident to theregion at a first angle. This angle is relative to the surface of thetissue sample at a point on the region, for example. Then, the topillumination source 188 is turned off while the bottom illuminationsource 190 is turned on, such that spectral measurements may be obtainedusing light incident to the region at a second angle. If data obtainedusing one of the illumination sources is adversely affected by anartifact, such as glare or shadow, then data obtained using anotherillumination source, with light incident to the region at a differentangle, may be unaffected by the artifact and may still be useful. Thespectral measurements can include reflectance and/or fluorescence dataobtained over a range of wavelengths.

[0194] According to the various illustrative embodiments, the top andthe bottom illumination sources 188, 190 may be alternately cycled onand off more than once while obtaining data for a given region. Also,cycles of the illumination sources 188, 190 may overlap, such that morethan one illumination source is on at one time for at least part of theillumination collection procedure. Other illumination alternationschemes are possible, depending at least in part on the arrangement ofillumination sources 188, 190 in relation to the probe head 192.

[0195] After data are obtained from one region of the tissue using lightincident to the region at more than one angle, data may likewise beobtained from another region of the tissue. In the illustrativeembodiment of FIG. 4, the scanner assembly 180 illuminates a target areaof the tissue sample region-by-region. Illustratively, a first region isilluminated using light incident to the region at more than one angle asdescribed above, then the probe optics 178 are automatically adjusted torepeat the illumination sequence at a different region within the targetarea of the tissue sample. The illustrative process is repeated until adesired subset of the target area has been scanned. As mentioned above,preferably about five hundred regions are scanned within a target areahaving a diameter of about 25-mm. Using the instrument 102, the scan ofthe aforementioned five hundred regions takes about 12 seconds. In otherillustrative embodiments, the number of regions scanned, the size of thetarget area, and/or the duration of the scan vary from the above.

[0196]FIG. 5 depicts an exemplary scan pattern 202 used by theinstrument 102 to obtain spatially-correlated spectral data and imagedata from a tissue sample according to an illustrative embodiment of theinvention. Illustratively, spectral data are obtained at 499 regions ofthe tissue sample, plus one region out of the field of view of thecervix obtained, for example, for calibration purposes. The exemplaryscan pattern 202 of FIG. 5 includes 499 regions 204 whose centers areinside a circle 206 that measures about 25.8 mm in diameter. The centerof each region is about 1.1 mm away from each of the nearest surroundingregions. This may be achieved by offsetting each scan line by about0.9527 mm in the y-direction and by staggering each scan line in thex-direction by about 0.55 mm. Each of the 499 regions is about 0.7 mm indiameter. In other illustrative embodiments, other geometries are used.

[0197] According to the illustrative embodiment, the spectral dataacquisition component 104 of the system 100 depicted in FIG. 1 isperformed using the scan pattern 202 shown in FIG. 5. A fluorescencespectrum, two broadband reflectance spectra, and a reference spectrumare obtained at each region 204. The two broadband reflectance spectrause light incident to the sample at two different angles. A scanpreferably begins at the center region 208, which corresponds to a pixelin a 500×480 pixel video image of the tissue sample at location 250,240. As discussed in more detail below, a sequence of video images ofthe tissue sample may be taken during a scan of the 499 regions shown inFIG. 5 and may be used to detect and compensate for movement of thetissue sample during the scan. The real-time tracker component 106 ofthe system 100 shown in FIG. 1 performs this motion detection andcompensation function. Preferably, the scanner assembly 180 of FIG. 3includes controls for keeping track of the data obtained, detecting astalled scan process, aborting the scan if the tissue is exposed totemperature or light outside of acceptable ranges, and/or monitoring andreporting errors detected by the spectral data acquisition component 104of the system of FIG. 1.

[0198]FIG. 6 depicts front views of four exemplary arrangements 210,212, 214, 216 of illumination sources about a probe head 192 accordingto various illustrative embodiments of the invention. The drawings arenot to scale; they serve to illustrate exemplary relative arrangementsof illumination sources about the perimeter of a probe head 192. Otherarrangements include positioning collecting optics 200 around theperimeter of the probe head 192, about the illumination sources, or inany other suitable location relative to the illumination sources. Thefirst arrangement 210 of FIG. 6 has one top illumination source 218 andone bottom illumination source 220, which are alternately cycled on andoff as described above. The illumination sources are arranged about thecollecting optics 200, which are located in the center of the probe head192. Light from an illumination source is reflected from the tissue andcaptured by the collecting optics 200.

[0199] The second arrangement 212 of FIG. 6 is similar to the firstarrangement 210, except that there are two illumination sources 222, 224in the top half of the probe head 192 and two illumination sources 226,228 in the bottom half of the probe head 192. In one embodiment, the twolights above the midline 230 are turned on and the two lights below themidline 230 are turned off while obtaining a first set of spectral data;then the lights above the midline 230 are turned off and the lightsbelow the midline 230 are turned on while obtaining a second set ofspectral data. In an alternate illustrative embodiment, only one of thefour illumination sources are turned on at a time to obtain four sets ofspectral data for a given region. Other illustrative embodiments includeturning the illumination sources on and off in other patterns. Otheralternative embodiments include using noncircular or otherwisedifferently shaped illumination sources, and/or using a different numberof illumination sources.

[0200] The third arrangement 214 of FIG. 6 includes each illuminationsource 232, 234 positioned on either side of the probe head 192. Thesources 232, 234 may be alternated in a manner analogous to thosedescribed for the first arrangement 210.

[0201] The fourth arrangement 216 of FIG. 6 is similar to the secondarrangement 212, except that the illumination sources 236, 238 on theright side of the probe head 192 are turned off and on together,alternately with the illumination sources 240, 242 on the left side ofthe probe head 192. Thus, two sets of spectral data may be obtained fora given region, one set using the illumination sources 236, 238 on theright of the midline 244, and the other set using the illuminationsources 240, 242 on the left of the midline 244.

[0202]FIG. 7 depicts exemplary illumination of a region 250 of a tissuesample 194 using light incident to the region 250 at two differentangles 252, 254 according to an illustrative embodiment of theinvention. FIG. 7 demonstrates that source light position may affectwhether data is affected by glare. The probe head 192 of FIG. 7 isdepicted in a cut-away view for illustrative purposes. In thisillustrative embodiment, the top illumination source 188 and bottomillumination source 190 are turned on sequentially and illuminate thesurface of a tissue sample 194 at equal and opposite angles relative tothe collection axis 256. Arrows represent the light emitted 252 from thetop illumination source 188, and the light specularly reflected 258 fromthe surface of the region 250 of the tissue sample 194. In preferredembodiments, it is desired to collect diffusely reflected light, asopposed to specularly reflected light 258 (glare). Since the specularlyreflected light 258 from the top illumination source 188 does not enterthe collecting optics 200 in the example illustrated in FIG. 7, a set ofdata obtained using the top illumination source 188 would not beaffected by glare.

[0203] However, in the example illustrated in FIG. 7, the emitted light254 from the bottom illumination source 190 reaches the surface of theregion 250 of the tissue 194 and is specularly reflected into thecollecting optics 200, shown by the arrow 260. Data obtained using thebottom illumination source 190 in the example pictured in FIG. 7 wouldbe affected by glare. This data may not be useful, for example, indetermining a characteristic or a condition of the region 250 of thetissue 194. In this example, it would be advantageous to instead use theset of data obtained using the top illumination source 188 since it isnot affected by glare.

[0204] The position of the collection optics 200 may affect whether ornot data is affected by glare. For example, light 252 with illuminationintensity I₀(λ) strikes a tissue surface at a given region 250. Afraction of the initial illumination intensity, αI₀(λ), is specularlyreflected from the surface 258, where α is a real number between 0and 1. An acceptance cone 268 is the space through which light isdiffusely reflected from the tissue 194 into the collecting optics 200,in this embodiment. Light may also be emitted or otherwise transmittedfrom the surface of the tissue. The diffusely reflected light is ofinterest, since spectral data obtained from diffusely reflected lightcan be used to determine the condition of the region of the sample. Ifthere is no specular reflection within the acceptance cone 268, onlydiffusely reflected light is collected, and the collected signalcorresponds to I_(t)(λ), where I_(t)(λ) is the intensity of lightdiffusely reflected from the region 250 on the surface of the tissue.

[0205] If the collection optics 200 are off-center, light incident tothe tissue surface may specularly reflect within the acceptance cone268. For example, light with illumination intensity I₀(λ) strikes thesurface of the tissue. Light with a fraction of the initial illuminationintensity, αI₀(λ), from a given source is specularly reflected from thesurface 266, where a is a real number between 0 and 1. Where there isspecular reflection of light within the acceptance cone 268, bothdiffusely reflected light and specularly reflected light reach thecollecting optics 200. Thus, the collected signal corresponds to anintensity represented by the sum I_(t)(λ)+αI₀(λ). It may be difficult orimpossible to separate the two components of the measured intensity,thus, the data may not be helpful in determining the condition of theregion of the tissue sample due to the glare effect.

[0206]FIG. 8 is a diagram 284 depicting illumination of a region 250 ofa cervical tissue sample 194 using a probe 142 and a vaginal speculum286 according to an illustrative embodiment of the invention. Here, theilluminating light incident to the tissue sample 194, is depicted by theupper and lower intersecting cones 196, 198. In a preferred embodiment,the probe 142 operates without physically contacting the tissue beinganalyzed. In one embodiment, a disposable sheath 146 is used to coverthe probe head 192, for example, in case of incidental contact of theprobe head 192 with the patient's body. FIG. 9 is a schematicrepresentation of an accessory device 290 that forms at least part ofthe disposable sheath 146 for a probe head 192 according to anillustrative embodiment of the invention. In one illustrativeembodiment, the entire sheath 146, including the accessory device 290,if present, is disposed of after a single use on a patient. As shown inFIG. 8, in one illustrative embodiment, the disposable sheath 146 and/orthe accessory device 290 have a unique identifier, such as atwo-dimensional bar code 292. According to an illustrative feature, theaccessory device 290 is configured to provide an optimal light pathbetween the optical probe 142 and the target tissue 194. Optionaloptical elements in the accessory device 290 may be used to enhance thelight transmitting and light receiving functions of the probe 142.

[0207] Although an illustrative embodiment of the invention is describedherein with respect to analysis of vaginal tissue, other tissue typesmay be analyzed using these methods, including, for example, colorectal,gastroesophageal, urinary bladder, lung, skin tissue, and/or any tissuecomprising epithelial cells.

Spectral Calibration—110, 112, 116

[0208]FIG. 10 is a block diagram 300 featuring components of the tissuecharacterization system 100 of FIG. 1 that involve spectral datacalibration and correction, according to an illustrative embodiment ofthe invention. The instrument 102 of FIG. 1 is calibrated at thefactory, prior to field use, and may also be calibrated at regularintervals via routine preventive maintenance (PM). This is referred toas factory and/or preventive maintenance calibration 110. Additionally,calibration is performed immediately prior to each patient scan toaccount for temporal and/or intra-patient sources of variability. Thisis referred to as pre-patient calibration 116. The illustrativeembodiment includes calibrating one or more elements of the instrument102, such as the spectrometer and detector 168 depicted in FIG. 3.

[0209] Calibration includes performing tests to adjust individualinstrument response and/or to provide corrections accounting forindividual instrument variability and/or individual test (temporal)variability. During calibration procedures, data is obtained for thepre-processing of raw spectral data from a patient scan. The tissueclassification system 100 of FIG. 1 includes determining correctionsbased on the factory and/or preventive maintenance calibration tests,indicated by block 112 in FIG. 10 and in FIG. 1. Where multiple sets offactory and/or preventive maintenance (PM) data exists, the most recentset of data is generally used to determine correction factors and topre-process spectral data from a patient scan. Corrections are alsodetermined based on pre-patient calibration tests, indicated by block118 of FIG. 10. The correction factors are used, at least indirectly, inthe pre-processing (114, FIG. 1) of fluorescence and reflectancespectral data obtained using a UV light source and two white lightsources. Block 114 of FIG. 11 corresponds to the pre-processing ofspectral data in the overall tissue classification system 100 of FIG. 1,and is further discussed herein.

[0210] Calibration accounts for sources of individual instrumentvariability and individual test variability in the preprocessing of rawspectral data from a patient scan. Sources of instrument and individualtest variability include, for example, external light (light originatingoutside the instrument 102, such as room light) and internal straylight. Internal stray light is due at least in part to internal “crosstalk,” or interaction between transmitted light and the collectionoptics 200. Calibration also accounts for the electronic backgroundsignal read by the instrument 102 when no light sources, internal orexternal, are in use. Additionally, calibration accounts for variationsin the amount of light energy delivered to a tissue sample during ascan, spatial inhomogeneities of the illumination source(s), chromaticaberration due to the scanning optics, variation in the wavelengthresponse of the collection optics 200, and/or the efficiency of thecollection optics 200, for example, as well as other effects.

[0211] In the illustrative embodiment of FIG. 10, factory and preventivemaintenance calibration tests are performed to determine correctionfactors 112 to apply to raw fluorescence and reflectance spectral dataobtained during patient scans. The factory/preventive maintenancecalibration tests 110 include a wavelength calibration test 302, a“null” target test 304, a fluorescent dye cuvette test 306, a tungstensource test 308, an “open air” target test 310, a customized target test312, and a NIST standard target test 314.

[0212] The wavelength calibration test 302 uses mercury and argonspectra to convert a CCD pixel index to wavelengths (nm). A wavelengthcalibration and interpolation method using data from the mercury andargon calibration test 302 is described below.

[0213] The null target test 304 employs a target having about 0% diffusereflectivity and is used along with other test results to account forinternal stray light. Data from the factory/PM null target test 304 areused to determine the three correction factors shown in block 316 forfluorescence spectral measurements (F) obtained using a UV light source,and broadband reflectance measurements (BB1, BB2) obtained using each oftwo white light sources. In one embodiment, these three correctionfactors 316 are used in determining correction factors for other tests,including the factory/PM fluorescent dye cuvette test 306, thefactory/PM open air target test 310, the factory/PM customized targettest 312, and the factory/PM NIST standard target test 314. The open airtarget test 310, the customized target test 312, and the NIST standardtarget test 314 are used along with the null target test 304 to correctfor internal stray light in spectral measurements obtained using a UVlight source and one or more white light sources.

[0214] The open air target test 310 is performed without a target and inthe absence of external light (all room lights turned off). Thecustomized target test 312 employs a custom-designed target including amaterial of approximately 10% diffuse reflectivity and is performed inthe absence of external light. The custom-designed target also containsphosphorescent and fluorescent plugs that are used during instrumentfocusing and target focus validation 122. In one embodiment, thecustom-designed target is also used during pre-patient calibrationtesting (116, 330) to monitor the stability of fluorescence readingsbetween preventive maintenance procedures and/or to align an ultraviolet(UV) light source 160—for example, a nitrogen laser or afrequency-tripled Nd:YAG laser. The NIST (U.S. National Institute ofStandards and Technology) standard target test 314 employs aNIST-standard target comprising a material of approximately 60% diffusereflectivity and is performed in the absence of external light.Correction factors determined from the “open air” target test 310, thecustom target test 312, and the NIST-standard target test 314 are shownin blocks 322, 324, and 326 of FIG. 10, respectively. The correctionfactors are discussed in more detail below.

[0215] The fluorescent dye cuvette test 306 accounts for the efficiencyof the collection optics 200 of a given unit. The illustrativeembodiment uses data from the fluorescent dye cuvette test 306 todetermine a scalar correction factor 318 for fluorescence measurements(F) obtained using a UV light source. The tungsten source test 308 usesa quartz-tungsten-halogen lamp to account for the wavelength response ofthe fluorescence collection optics 200, and data from this test are usedto determine a correction factor 320 for fluorescence measurements (F)obtained using a UV light source.

[0216] In addition to factory and preventive maintenance calibration110, pre-patient calibration 116 is performed immediately before eachpatient scan. The pre-patient calibration 116 includes performing a nulltarget test 328 and a customized target test 330 before each patientscan. These tests are similar to the factory/PM null target test 304 andthe factory/PM custom target test 312, except that they are eachperformed under exam room conditions immediately before a patient scanis conducted. The correction factors shown in blocks 332 and 334 of FIG.10 are determined from the results of the pre-patient calibration tests.Here, correction factors (316, 322) from the factory/PM null target test304 and the factory/PM open air test 310 are used along with pre-patientcalibration data to determine the pre-patient correction factors 118,which are used, in turn, to pre-process raw spectral data from a patientscan, as shown, for example, in FIG. 1.

[0217]FIG. 11 is a block diagram 340 featuring the spectral datapre-processing component 114 of the tissue characterization system 100of FIG. 1 according to an illustrative embodiment of the invention. InFIG. 11, “F” represents the fluorescence data obtained using the UVlight source 160, “BB1” represents the broadband reflectance dataobtained using the first 188 of the two white light sources 162 and“BB2” represents the broadband reflectance data obtained using thesecond 190 of the two white light sources 162. Blocks 342 and 344indicate steps undertaken in pre-processing raw reflectance dataobtained from the tissue using each of the two white light sources 188,190, respectively. Block 346 indicates steps undertaken inpre-processing raw fluorescence data obtained from the tissue using theUV light source 160. These steps are discussed in more detail below.

[0218] The instrument 102 detailed in FIG. 3 features a scanner assembly180 which includes a CCD (charge couple device) detector andspectrograph for collecting fluorescence and reflectance spectra fromtissue samples. Because a CCD detector is used, the system employs acalibration procedure to convert a pixel index into wavelength units.Referring to FIG. 10, the pixel-to-wavelength calibration 302 isperformed as part of factory and/or preventive maintenance calibrationprocedures 110.

[0219] In the illustrative embodiment, the tissue classification system100 uses spectral data obtained at wavelengths within a range from about360 nm to about 720 mn. Thus, the pixel-to-wavelength calibrationprocedure 302 uses source light that produces peaks near and/or withinthe 360 nm to 720 nm range. A mercury lamp produces distinct, usablepeaks between about 365 nm and about 578 nm, and an argon lamp producesdistinct, usable peaks between about 697 nm and about 740 nm. Thus, theillustrative embodiment uses mercury and argon emission spectra toconvert a pixel index from a CCD detector into units of wavelength (nm).

[0220] First, a low-pressure pen-lamp style mercury lamp is used assource light, and intensity is plotted as a function of pixel index. Thepixel indices of the five largest peaks are correlated to ideal,standard Hg peak positions in units of nanometers. Second, a pen-lampstyle argon lamp is used as source light and intensity is plotted as afunction of pixel index. The two largest peaks are correlated to ideal,standard Ar peak positions in units of nanometers.

[0221] The seven total peaks provide a set of representative peakswell-distributed within a range from about 365 nm to about 738nm—comparable to the range from about 360 nm to about 720 nm that isused for data analysis in the tissue classification system 100. Thecalibration procedure in block 302 of FIG. 10 includes retrieving thefollowing spectra: a spectrum using a mercury lamp as light source, amercury background spectrum (a spectrum obtained with the mercury sourcelight turned off), a spectrum using an argon lamp as light source, andan argon background spectrum. The respective Hg and Ar backgroundspectra are subtracted from the Hg and Ar spectra, producing thebackground-corrected Hg and Ar spectra. The spectra are essentiallynoise-free and require no smoothing. Each of the seven pixel valuescorresponding to the seven peaks above are determined by finding thecentroid of the curve of each peak over a +/−5 pixel range of themaximum as shown in Equation 1: $\begin{matrix}{{{centroid} = \frac{\int_{p_{\max} - 5}^{p_{\max} + 5}{p\quad I_{p}\quad {p}}}{\int_{p_{\max} - 5}^{p_{\max} + 5}\quad {I_{p}\quad {p}}}},} & (1)\end{matrix}$

[0222] where p is pixel value, I_(p) is the intensity at pixel p, andp_(max) is the pixel value corresponding to each peak maximum. From thep_(max) determinations, a polynomial function correlating pixel value towavelength value is determined by performing a least-squares fit of thepeak data. In one embodiment, the polynomial function is of fourthorder. In alternative embodiments, the polynomial is of first order,second order, third order, fifth order, or higher order.

[0223] Alternatively to finding p_(max) by determining the centroid asdiscussed above, in another illustrative embodiment thepixel-to-wavelength calibration procedure 302 includes fitting a secondorder polynomial to the signal intensity versus pixel index data foreach of the seven peaks around the maximum +/−3 pixels (range including7 pixels); taking the derivative of the second order polynomial; andfinding the y-intercept to determine each p_(max).

[0224] The resulting polynomial function correlating pixel value towavelength value is validated, for example, by specifying that themaximum argon peak be located within a given pixel range, such as[300:340] and/or that the intensity count at the peak be within areasonable range, such as between 3000 and 32,000 counts. Additionally,the maximum mercury peak is validated to be between pixel 150 and 225and to produce an intensity count between 3000 and 32,000 counts. Next,the maximum difference between any peak wavelength predicted by thepolynomial function and its corresponding ideal (reference) peak isrequired to be within about 1.0 nm. Alternatively, other validationcriteria may be set.

[0225] Additional validation procedures may be performed to comparecalibration results obtained for different units, as well as stabilityof calibration results over time. In one illustrative embodiment, thepixel-to-wavelength calibration 302 and/or validation is performed aspart of routine preventive maintenance procedures.

[0226] Since fluorescence and reflectance spectral data that are used asreference data in the classification system 100 may be obtained atmultiple clinical sites with different individual instruments, theillustrative system 100 standardizes spectral data in step 302 of FIG.10 by determining and using values of spectral intensity only atdesignated values of wavelength. Spectral intensity values arestandardized by interpolating pixel-based intensities such that theycorrespond to wavelengths that are spaced every 1 nm between about 360nm and about 720 nm. This may be done by linear interpolation of thepixel-based fluorescence and/or reflectance values. Other illustrativeembodiments use, for example, a cubic spline interpolation procedureinstead of linear interpolation.

[0227] In some illustrative embodiments, spectral data acquisitionduring patient scans and during the calibration procedures of FIG. 10includes the use of a CCD array as part of the scanner assembly 180depicted in FIG. 3. The CCD array may contain any number of pixelscorresponding to data obtained at a given time and at a giveninterrogation point. In one embodiment, the CCD array contains about 532pixels, including unused leading pixels from index 0 to 9, relevant datafrom index 10 to 400, a power monitor region from index 401 to 521, andunused trailing pixels from index 522 to 531. One embodiment includes“power correcting” or “power monitor correcting” by scaling rawreflectance and/or fluorescence intensity measurements received from aregion of a tissue sample with a measure of the intensity of lighttransmitted to the region of the tissue sample. In order to provide thescaling factor, the instrument 102 directs a portion of a light beamonto the CCD array, for example, at pixel indices 401 to 521, andintegrates intensity readings over this portion of the array.

[0228] In one preferred embodiment, both factory/PM 110 and pre-patient116 calibration accounts for chromatic, spatial, and temporalvariability caused by system interference due to external stray light,internal stray light, and electronic background signals. External straylight originates from sources external to the instrument 102, forexample, examination room lights and/or a colposcope light. Theoccurrence and intensity of the effect of external stray light onspectral data is variable and depends on patient parameters and theoperator's use of the instrument 102. For example, as shown in FIG. 8,the farther the probe head 192 rests from the speculum 286 in theexamination of cervical tissue, the greater the opportunity for roomlight to be present on the cervix. The configuration and location of adisposable component 146 on the probe head 192 also affects externalstray light that reaches a tissue sample. Additionally, if the operatorforgets to turn off the colposcope light before taking a spectral scan,there is a chance that light will be incident on the cervix and affectspectral data obtained.

[0229] Electronic background signals are signals read from the CCD arraywhen no light sources, internal or external, are in use. According tothe illustrative embodiment, for all components of the tissuecharacterization system 100 that involve obtaining and/or using spectraldata, including components 110, 116, 104, and 114 of FIG. 1, bothexternal stray light and electronic background signals are taken intoaccount by means of a background reading. For each interrogation pointin a spectral scan in which one or more internal light sources are used,a background reading is obtained in which all internal light sources(for example, the Xenon lamps and the UV laser) are turned off.According to one feature, the background reading immediately precedesthe fluorescence and broadband reflectance measurements at each scanlocation, and the system 100 corrects for external stray light andelectronic background by subtracting the background reading from thecorresponding spectral reading at a given interrogation point. In FIG.10, each calibration test—including 304, 306, 308, 310, 312, 314, 328,and 330—includes obtaining a background reading at each interrogationpoint and subtracting it from the test reading to account for externalstray light and electronic background signals. Also, backgroundsubtraction is a step in the spectral data preprocessing 114 methods inFIG. 11, for the pre-processing of raw BB1 and BB2 reflectance data 342,344 as well as the pre-processing of raw fluorescence data 346.

[0230] Equation 2 shows the background correction for a generic spectralmeasurement from a tissue sample, S_(tissue+ISL+ESL+EB)(i,λ)

S _(tissue+ISL)(i,λ)=S _(tissue+ISL+ESL+EB)(i,λ)−Bk _(EB+ESL)(i,λ)  (2)

[0231] where i corresponds to a scan location; λ is wavelength or itspixel index equivalent; and subscripts denote influences on the spectralmeasurement—where “tissue” represents the tissue sample, “ISL”represents internal stray light (internal to the instrument 102), “ESL”represents external stray light, and “EB” represents electronicbackground. S_(tissue+ISL+ESL+EB)(i,λ), is a two-dimensional array(which may be power-monitor corrected) of spectral data obtained fromthe tissue at each interrogation point (region) i as a function ofwavelength λ; and Bk_(EB+ESL)(i,λ) is a two-dimensional arrayrepresenting values of the corresponding background spectral readings ateach point i as a function of wavelength λ. S_(tissue+ISL)(i,λ) is thebackground-subtracted spectral array that is thereby corrected foreffects of electronic background (EB) and external stray light (ESL) onthe spectral data from the tissue sample. The electronic backgroundreading is subtracted on a wavelength-by-wavelength,location-by-location basis. Subtracting the background reading generallydoes not correct for internal stray light (ISL), as denoted in thesubscript of S_(tissue+ISL)(i,λ).

[0232] Internal stray light includes internal cross talk and interactionbetween the transmitted light within the system and the collectionoptics. For fluorescence measurements, a primary source of internalstray light is low-level fluorescence of optics internal to the probe142 and the disposable component 146. For reflectance measurements, aprimary source of internal stray light is light reflected off of thedisposable 146 and surfaces in the probe 142 that is collected throughthe collection optics 200. The positioning of the disposable 146 cancontribute to the effect of internal stray light on reflectancemeasurements. For example, the internal stray light effect may vary overinterrogation points of a tissue sample scan in a non-random,identifiable pattern due to the position of the disposable during thetest.

[0233] According to the illustrative embodiment of FIG. 10, thefactory/PM null target test 304, the factory/PM open air target test306, the factory/PM custom target test 312, the factory/PM NIST targettest 314, the pre-patient null target test 328, and the pre-patientcustom target test 330 provide correction factors to account forinternal stray light effects on fluorescence and reflectance spectralmeasurements. In an alternative illustrative embodiment, a subset ofthese tests is used to account for internal stray light effects.

[0234] The null target test 304, 328, performed in factory/preventivemaintenance 110, and pre-patient 116 calibration procedures, uses atarget that has a theoretical diffuse reflectance of 0%, although theactual value may be higher. Since, at least theoretically, no light isreflected by the target, the contribution of internal stray light can bemeasured for a given internal light source by obtaining a spectrum froma region or series of regions of the null target with the internal lightsource turned on, obtaining a background spectrum from the null targetwith the internal light source turned off, and background-subtracting toremove any effect of electronic background signal or external straylight. The background-subtracted reading is then a measure of internalstray light. The pre-patient null target test 328 takes into accountspatially-dependent internal stray light artifacts induced by theposition of a disposable 146, as well as temporal variability induced,for example, by the aging of the instrument and/or dust accumulation. Inone embodiment, the factory/PM null target test 304 is used incalculating correction factors from other factory and/or preventivemaintenance calibration procedures. The null target tests 304, 328 arenot perfect, and improved measurements of the effect of internal straylight on spectral data can be achieved by performing additional tests.

[0235] The open air target test 310 is part of the factory preventivemaintenance (PM) calibration procedure 110 of FIG. 10 and provides acomplement to the null target tests 304, 328. The open air target test310 obtains data in the absence of a target with the internal lightsources turned on and all light sources external to the device turnedoff, for example, in a darkroom. The null target test 304, by contrast,does not have to be performed in a darkroom since it uses a target inplace in the calibration port, thereby sealing the instrument such thatmeasurements of light from the target are not affected by externallight. Although a disposable 146 is in place during open air testmeasurements, the factory/PM open air target test 310 does not accountfor any differences due to different disposables used in each patientrun. The open air measurements are important in some embodiments,however, since they are performed under more controlled conditions thanpre-patient calibration tests 116, for example, the open air tests maybe performed in a darkroom. Also, the factory/PM calibration 110measurements account for differences between individual instruments 102,as well as the effects of machine aging—both important factors sincereference data obtained by any number of individual instruments 102 arestandardized for use in a tissue classification algorithm, such as theone depicted in block 132 of FIG. 1.

[0236]FIGS. 12, 13, 14, and 15 show graphs demonstrating meanbackground-subtracted, power-monitor-corrected intensity readings from afactory open air target test 310 and a null target test 304 using a BB1reflectance white light source and a UV light source (laser). FIG. 12shows a graph 364 of mean intensity 366 from an open air target testover a set of regions as a function of wavelength 368 using a BB1reflectance white light source 188—the “top” source 188 as depicted inFIGS. 4, 7, and 8. FIG. 13 shows a graph 372 of mean intensity 366 froma null target test over the set of regions as a function of wavelength368 using the same BB1 light source. Curves 370 and 374 are comparablebut there are some differences.

[0237]FIG. 14 shows a graph 376 of mean intensity 378 from an open airtarget test over a set of regions as a function of wavelength 380 usinga UV light source, while FIG. 15 shows a graph 384 of mean intensity 378from a null target test over the set of regions as a function ofwavelength 380 using the UV light source. Again, curves 382 and 386 arecomparable, but there are some differences between them. Differencesbetween the open air test intensity and null target test intensity aregenerally less than 0.1% for reflectance data and under 1 count/μJ forfluorescence data.

[0238] Accounting for internal stray light is more complicated forreflectance measurements than for fluorescence measurements due to anincreased spatial dependence. The open air target test measurement, inparticular, has a spatial profile that is dependent on the position ofthe disposable.

[0239]FIG. 16 shows a representation 390 of regions of an exemplary scanperformed in a factory open air target test. The representation 390,shows that broadband intensity readings can vary in a non-random,spatially-dependent manner. Other exemplary scans performed in factoryopen air target tests show a more randomized, less spatially-dependentvariation of intensity readings than the scan shown in FIG. 16.

[0240] According to the illustrative embodiment, the system 100 of FIG.1 accounts for internal stray light by using a combination of theresults of one or more open air target tests 310 with one or more nulltarget tests 304, 328. In an alternative embodiment, open air targettest data is not used at all to correct for internal stray light,pre-patient null target test data being used instead.

[0241] Where open air and null target test results are combined, it ishelpful to avoid compounding noise effects from the tests. FIG. 17 showsa graph 402 depicting as a function of wavelength 406 the ratio 404 ofthe background-corrected, power-monitor-corrected reflectance spectralintensity at a given region using an open air target to the reflectancespectral intensity at the region using a null target according to anillustrative embodiment of the invention. The raw data 407 is shown inFIG. 17 fit with a second-order polynomial 412, and fit with athird-order polynomial without filtering 410, and with filtering 408. Asseen by the differences between curve 407 and curves 408, 410, and 412,where a ratio of open air target data and null target data are used tocorrect for internal stray light in reflectance measurements, a curvefit of the raw data reduces the effect of noise. This is shown in moredetail herein with respect to the calculation of pre-patient corrections118 in FIG. 10. Also evident in FIG. 17 is that the open air measurementgenerally differs from the null target measurement, since the ratio 404is not equal to 1, and since the ratio 404 has a distinct wavelengthdependence.

[0242]FIG. 18 shows a graph 414 depicting as a function of wavelength418 the ratio 416 of fluorescence spectral intensity using an open airtarget to the fluorescence spectral intensity using a null targetaccording to an illustrative embodiment of the invention. The raw data420 does not display a clear wavelength dependence, except that noiseincreases at higher wavelengths. A mean 422 based on the ratio data 420over a range of wavelengths is plotted in FIG. 18. Where a ratio of openair target to null target data is used to correct for internal straylight in fluorescence measurements, using a mean value calculated fromraw data over a stable range of wavelength reduces noise and does notignore any clear wavelength dependence.

[0243]FIG. 10 shows correction factors corresponding to open air 310 andnull target 304, 328 calibration tests in one embodiment thatcompensates spectral measurements for internal stray light effects.There are three types of spectral measurements in FIG. 10—fluorescence(F) measurements and two reflectance measurements (BB1, BB2)corresponding to data obtained using a UV light source and two differentwhite light sources, respectively. The corrections in blocks 316, 322,and 332 come from the results of the factory/PM null target test 304,the factory/PM open air target test 310, and the pre-patient null targettest 328, respectively, and these correction factors are applied inspectral data pre-processing (FIG. 11) to compensate for the effects ofinternal stray light. These correction factors are described below interms of this embodiment.

[0244] Block 316 in FIG. 10 contains correction factors computed fromthe results of the null target test 304, performed during factory and/orpreventive maintenance (PM) calibration. The null target test includesobtaining a one-dimensional array of mean values of spectral data fromeach channel—F, BB1, and BB2—corresponding to the three different lightsources, as shown in Equations 3, 4, and 5:

FCNULLFL=

I _(nt,F)(i,λ,t _(o))

_(i)  (3)

FCNULLBB1=

I _(nt,BB1)(i,λ,t _(o))

_(i)  (4)

FCNULLBB2=

I _(nt,BB)2(i,λ,t _(o))

_(i)  (5)

[0245] where I_(nt) refers to a background-subtracted,power-monitor-corrected two-dimensional array of spectral intensityvalues; subscript F refers to intensity data obtained using thefluorescence UV light source; subscripts BB1 and BB2 refer to intensitydata obtained using the reflectance BB1 and BB2 white light sources,respectively; i refers to interrogation point “i” on the calibrationtarget; λ refers to a wavelength at which an intensity measurementcorresponds or its approximate pixel index equivalent; t_(o) refers tothe fact the measurement is obtained from a factory or preventivemaintenance test, the “time” the measurement is made; and

_(i) represents a one-dimensional array (spectrum) of mean valuescomputed on a pixel-by-pixel basis for each interrogation point, i. Inthis embodiment, a one-dimensional array (spectrum) of fluorescencevalues corresponding to wavelengths from λ=370 nm to λ=720 nm isobtained at each of 499 interrogation points, i. An exemplary scanpattern 202 of 499 interrogation points appears in FIG. 5. In theillustrative embodiment, data from an additional interrogation point isobtained from a region outside the target 206. Each of the reflectanceintensity spectra is obtained over the same wavelength range as thefluorescence intensity spectra, but the BB1 data is obtained at each of250 interrogation points over the bottom half of the target and the BB2data is obtained at each of 249 interrogation points over the top halfof the target. This avoids a shadowing effect due to the angle at whichthe light from each source strikes the target during the null targettest 304. Values of the most recent factory or preventive maintenancecalibration test, including the factory/PM null target test 304, areused in spectral data pre-processing (FIG. 11) for each patient scan.

[0246] The pre-patient null target test, shown in block 328 of FIG. 10,is similar to the factory/PM null target test 304, except that it isperformed just prior to each patient test scan. Each pre-patient nulltarget test 328 produces three arrays of spectral data as shown below:

I_(nt,F)(i,λ,t′)  (6)

I _(nt,BB1)(i,λ,t′)  (7)

I_(nt,BB2)(i,λ,t′)  (8)

[0247] where t′ refers to the fact the measurements are obtained justprior to the test patient scan, as opposed to during factory/PM testing(t_(o)).

[0248] Block 332 in FIG. 10 contains correction factors from the openair target test 310, preformed during factory and/or preventivemaintenance (PM) calibration 110. The open air target test is performedwith the disposable in place, in the absence of a target, with theinternal light sources turned on, and with all light sources external tothe device turned off. The open air target test 310 includes obtainingan array of spectral data values from each of the three channels—F, BB1,and BB2—as shown below:

I_(oa,F)(i,λ,t_(o))  (9)

I_(oa,BB1)(i,λ,t_(o))  (10)

I_(oa,BB2)(i,λ,t_(o))  (11)

[0249] In each of items 9, 10, and 11 above, I_(oa) refers to abackground-subtracted, power-monitor-corrected array of spectralintensity values; i runs from interrogation points 1 to 499; and λ runsfrom 370 nm to 720 nm (or the approximate pixel index equivalent).

[0250] According to the illustrative embodiment, correction for internalstray light makes use of both null target test results and open airtarget test results. Correction factors in block 322 of FIG. 10 useresults from the factory/PM null target test 304 and factory/PM open airtarget test 310. The correction factors in block 322 are computed asfollows:

sFCOFL=[

I _(oa,F)(i,λ,t _(o))

_(i)

/

I _(nt,F)(i,λ,t _(o))

_(i)]_(mean, λ=)375 nm to 470 nm  (12)

FCOBB1=fitted form of

I_(oa,BB1)(i,λ,t _(o))

_(i)/

I_(nt,BB1)(i,λ,t _(o))

_(i)  (13)

FCOBB2=fitted form of

I_(oa,BB2)(i,λ,t _(o))

_(i) /I _(nt,BB2)(i,λ,t _(o))

_(i)  (14)

[0251] where

_(i) represents a spectrum (1-dimensional array) of mean values computedon a pixel-by-pixel basis for each interrogation point i, and where

_(i)/

_(i) represents a spectrum (1-dimensional array) of quotients (ratios ofmeans) computed on a pixel-by-pixel basis for each interrogation pointi. The correction factor sFCOFL in Equation 12 is a scalar quantityrepresenting the mean value of the 1-dimensional array in brackets [ ]across pixel indices corresponding to the wavelength range of about 375nm to about 470 nm.

[0252]FIG. 18 shows an example value of sFCOFL 422 evaluated using a setof mean open air spectral data and mean null target spectral data. Largeoscillations are damped by using the mean in Equation 12. Otherwavelength ranges can be chosen instead of the wavelength range of about375 nm to about 470 nm.

[0253] The one-dimensional arrays, FCOBB1 and FCOBB2, are obtained bycurve-fitting the spectra of quotients in Equations 13 and 14 withsecond-order polynomials and determining values of the curve fitcorresponding to each pixel. FIG. 17 shows an example curve fit forFCOBB1 (412). Unlike the fluorescence measurements, there is wavelengthdependence of this ratio, and a curve fit is used to properly reflectthis wavelength dependence without introducing excessive noise infollowing computations.

[0254] Block 332 in FIG. 10 contains correction factors using resultsfrom the pre-patient null target test 328, as well as the most recentfactory/PM null target test 304 and open air target test 310. Thecorrection factors in block 332 are computed as follows:

SLFL=sFCOFL·

I _(nt,F)(

i,λ,t′)

_(i)  (15)

SLBB1=FCOBB1

·

I _(nt,BB1)·(i,λ,t′)

_(i)  (16)

SLBB2=FCOBB2

·

I _(nt,BB2)(i,λ,t′)

_(i)  (17)

[0255] where Equation 15 represents multiplying each value in thefluorescence mean pre-patient null target spectrum by the scalarquantity sFCOFL from Equation 12; Equation 16 represents multiplyingcorresponding elements of the mean pre-patient null target BB1 spectrumand the one-dimensional array FCOBB1 from Equation 13; and Equation 17represents multiplying corresponding elements of the mean pre-patientnull target BB2 spectrum and the one-dimensional array FCOBB2 fromEquation 14. Each of SLFL, SLBB1, and SLBB2 is a one-dimensional array.

[0256] The correction factors in block 332 of FIG. 10 represent thecontribution due to internal stray light (ISL) for a given set ofspectral data obtained from a given patient scan. Combining equationsabove:

SLFL=[

I _(oa,F)(i,λ,t _(o))

_(i)

/

I _(nt,F)(i,λ,t _(o))

_(i)]_(mean, λ=375 nm to 470 nm)

·

I _(nt,F)(i,λ,t′)

_(i)  (18)

SLBB1

=[

I _(oa,BB1)(i,λ,t _(o))

_(i)

/

I _(nt,BB1)(i,λ,t _(o))

_(i)]_(fitted)·

I_(nt,BB1)(i,λ,t′)

_(i)  (19)

SLBB2=[

I_(oa,BB2)(i,λ,t _(o))

_(i)

/

I _(nt,BB2)(i,λ,t _(o))

_(i)]_(fitted)

·

I _(nt,BB2)(i,λt′)

_(i)  (20)

[0257] Alternative internal stray light correction factors are possible.For example, in one alternative embodiment, the scalar quantity inEquation 18 is replaced with the value 1.0. In one alternativeembodiment, the first term on the right side of either or both ofEquation 19 and Equation 20 is replaced with a scalar quantity, forexample, a mean value or the value 1.0.

[0258] Spectral data preprocessing 114 as detailed in FIG. 11 includescompensating for internal stray light effects as measured by SLFL, SLBB1and SLBB2. In one embodiment, a patient scan includes the acquisition ateach interrogation point in a scan pattern (for example, the 499-pointscan pattern 202 shown in FIG. 5) of a set of raw fluorescence intensitydata using the UV light source 160, a first set of raw broadbandreflectance intensity data using a first white light source (162, 188),a second set of raw broadband reflectance intensity data using a secondwhite light source (162, 192), and a set of raw background intensitydata using no internal light source, where each set of raw data spans aCCD pixel index corresponding to a wavelength range between about 370 nmand 720 nm. In another embodiment, the wavelength range is from about370 nm to about 700 nm. In another embodiment, the wavelength range isfrom about 300 nm to about 900 nm. Other embodiments include the use ofdifferent wavelength ranges.

[0259] The raw background intensity data set is represented as thetwo-dimensional array Bkgnd[ ] in FIG. 11. Spectral data processing 114includes subtracting the background array, Bkgnd[ ], from each of theraw BB1, BB2, and F arrays on a pixel-by-pixel and location-by-locationbasis. This accounts at least for electronic background and externalstray light effects, and is shown as item #1 in each of blocks 342, 344,and 346 in FIG. 11.

[0260] Also, each CCD array containing spectral data includes a portionfor monitoring the power output by the light source used to obtain thespectral data. In one embodiment, the intensity values in this portionof each array are added or integrated to provide a one-dimensional arrayof scalar values, sPowerMonitor[ ], shown in FIG. 11. Spectral datapre-processing 114 further includes dividing each element of thebackground-subtracted arrays at a given interrogation point by the powermonitor scalar correction factor in sPowerMonitor[ ] corresponding tothe given interrogation point. This allows the expression of spectraldata at a given wavelength as a ratio of received light intensity totransmitted light intensity.

[0261] Spectral data pre-processing 114 further includes subtractingeach of the stray light background arrays—SLBB1, SLBB2, and SLFL—fromits corresponding background-corrected, power-monitor-corrected spectraldata array—BB1, BB2, and F—on a pixel-by-pixel, location-by-locationbasis. This accounts for chromatic, temporal, and spatial variabilityeffects of internal stray light on the spectral data.

[0262] The remaining steps in blocks 342 and 344 of the spectral datapre-processing block diagram 340 of FIG. 11 include further factory,preventive maintenance (PM) and/or pre-patient calibration ofreflectance (BB1, BB2) measurements using one or more targets of known,non-zero diffuse reflectance. In the embodiment shown in FIG. 10, thiscalibration uses results from the factory/PM custom target test 312, thefactory/PM NIST-standard target test 314, and the pre-patient customtarget test 330. These calibration tests provide correction factors asshown in blocks 324, 326, and 334 of FIG. 10, that account forchromatic, temporal, and spatial sources of variation in broadbandreflectance spectral measurements. These sources of variation includetemporal fluctuations in the illumination source, spatialinhomogeneities in the illumination source, and chromatic aberration dueto the scanning optics. The broadband reflectance calibration tests(312, 314, 330) also account for system artifacts attributable to bothtransmitted and received light, since these artifacts exist in both testreflectance measurements and known reference measurements.

[0263] According to the illustrative embodiment, reflectance, R,computed from a set of regions of a test sample (a test scan) isexpressed as in Equation 21:

R=[Measurement/Reference Target]·Reflectivity of Reference Target  (21)

[0264] where R, Measurement, and Reference Target refer totwo-dimensional (wavelength, position) arrays of background-corrected,power-corrected and/or internal-stray-light-corrected reflectance data;Measurement contains data obtained from the test sample; ReferenceTarget contains data obtained from the reference target; Reflectivity ofReference Target is a known scalar value; and division of the arrays isperformed in a pixel-by-pixel, location-by-location manner.

[0265] The factory/PM NIST target test 314 uses a 60%, NIST-traceable,spectrally flat diffuse reflectance target in the focal plane, alignedin the instrument 102 represented in FIG. 3. The NIST target test 314includes performing four scans, each of which proceed with the target atdifferent rotational orientations, perpendicular to the optical axis ofthe system. For example, the target is rotated 90° from one scan to thenext. The results of the four scans are averaged on alocation-by-location, pixel-by-pixel basis to remove spatially-dependenttarget artifacts (speckling) and to reduce system noise. The goal is tocreate a spectrally clean (low noise) and spatially-flat data set forapplication to patient scan data. In one embodiment, the NIST targettest 314 is performed only once, to prior to instrument 102 use in thefield (factory test), and thus, ideally, is temporally invariant.

[0266] The custom target tests 312, 330 use a custom-made target forboth factory and/or preventive maintenance calibration, as well aspre-patient calibration of reflectance data. The custom target is a 10%diffuse reflective target with phosphorescent and/or fluorescentportions used, for example, to align the ultraviolet (UV) light sourceand/or to monitor the stability of fluorescence readings betweenpreventive maintenance procedures. FIG. 19 is a photograph of the customtarget 426 according to an illustrative embodiment. In FIG. 19, thetarget 426 includes a portion 428 that is about 10% diffuse reflectivematerial, with four phosphorescent plugs 430, 432, 434, 436equally-spaced at the periphery and a single fluorescent plug 438 at thecenter. As a result of the plugs, not all scan locations in the scanpattern 202 of FIG. 5, as applied to the custom target test 426,accurately measure the 10% reflective portion. Thus, a mask provides ameans of filtering out the plug-influenced portions of the custom target426 during a custom target calibration scan 312, 330.

[0267]FIG. 20 is a representation of such a mask 444 for the customtarget reflectance calibration tests 312, 330. Area 445 in FIG. 20corresponds to regions of the custom target 426 of FIG. 19 that are notaffected by the plugs 430, 432, 434, 436, and which, therefore, areusable in the custom target reflectance calibration tests 312, 330.Areas 446, 448, 450, 452, and 454 of FIG. 20 correspond to regions ofthe custom target 426 that are affected by the plugs, and which aremasked out in the custom target calibration scan results.

[0268] In the illustrative embodiment, the factory/PM NIST target test314 provides reflectance calibration data for a measured signal from atest sample (patient scan), and the test sample signal is processedaccording to Equation 22:

R(i,λ,t′)=[I_(m)(i,λ,t′)/I _(fc)(i,λ,t _(o))]·0.6  (22)

[0269] Where R, I_(m), and I_(fc) are two-dimensional arrays ofbackground-corrected, power-corrected reflectance data; R containsreflectance intensity data from the test sample adjusted according tothe reflectance calibration data; I_(m) contains reflectance intensitydata from the sample, I_(fc) contains reflectance intensity data fromthe factory/PM NIST-standard target test 314, and 0.6 is the knownreflectivity of the NIST-standard target. Equation 22 presumes thespectral response of the illumination source is temporally invariantsuch that the factory calibration data from a given unit does not changewith time, as shown in Equation 23 below:

I _(fc)(t′)=I _(fc)(t _(o))  (23)

[0270] However, the spectral lamp function of a xenon flash lamp, asused in the illustrative embodiment as the white light source 162 in theinstrument 102 of FIG. 3, is not invariant over time.

[0271] The illustrative reflectance data spectral preprocessing 114accounts for temporal variance by obtaining pre-patient custom targettest (330) reflectance calibration data and using the data to adjustdata from a test sample, I_(m), to produce adjusted reflectance R, asfollows:

R(i,λ,t′)=[I _(m)(i,λ,t′)/

I _(cp)(i,λ,t′)

_(i)]·0.1  (24)

[0272] where masked, mean reflectance intensity data from thepre-patient custom target test 330 with 10% diffuse reflectivity,(I_(cp)(i,λ,t′)), replaces I_(fc)(i,λ,t′) in Equation 22. Since thepre-patient custom target test data is updated before every patientexam, the temporal variance effect is diminished or eliminated. In otherillustrative embodiments, various other reference targets may be used inplace of the custom target 426 shown in FIG. 19.

[0273] The system 100 also accounts for spatial variability in thetarget reference tests of FIG. 10 in pre-processing reflectance spectraldata. Illustratively, spatial variability in reflectance calibrationtarget intensity is dependent on wavelength, suggesting chromaticaberrations due to wavelength-dependence of transmission and/orcollection optic efficiency.

[0274] The illustrative reflectance data spectral preprocessing 114accounts for these chromatic and spatial variability effects byobtaining reflectance calibration data and using the data to adjust datafrom a test sample, I_(m), to produce adjusted reflectance R, asfollows:

R(i,λ,t′)=[I _(m)(i,λ,t′)/

I _(cp)(i,λ,t)

_(i)

]·[

I _(fc)(i,λ,t _(o))

_(i) /I _(fc)(i,λ,t _(o))]·0.1  (25)

[0275] Equation 25 accounts for variations of the intensity response ofthe lamp by applying the pre-patient custom-target measurements—whichare less dependent on differences caused by the disposable—in correctingpatient test sample measurements. Equation 25 also accounts for thespatial response of the illumination source by applying the factoryNIST-target measurements in correcting patient test sample measurements.

[0276] In an alternative illustrative embodiment, the NIST-target test314 is performed as part of pre-patient calibration 116 to producecalibration data, I_(fc)(i,λ,t′), and Equation 22 is used in processingtest reflectance data, where the quantity I_(fc)(i,λ,t′) replaces thequantity I_(fc)(i,λ,t_(o)) in Equation 22. According to thisillustrative embodiment, the test data pre-processing procedure 114includes both factory/PM calibration 110 results and pre-patientcalibration 116 results in order to maintain a more consistent basis forthe accumulation and use of reference data from various individual unitsobtained at various times from various patients in a tissuecharacterization system. Thus, this illustrative embodiment usesEquation 26 below to adjust data from a test sample, I_(m), to produceadjusted reflectance R, as follows:

R(i,λ,t′)=[I _(m)(i,λ,t′)/

I _(fc)(i,λ,t)

_(i)

]·[

I _(fc)(i,λ,t _(o))

_(i) /I _(fc)(i,λ,t _(o))]·0.6  (26)

[0277] where the NIST-standard target test 314 is performed both as afactory/PM test 110 (t_(o)) and as a pre-patient test 116 (t′).

[0278] According to the illustrative embodiment, it is preferable tocombine calibration standards with more than one target, each having adifferent diffuse reflectance, since calibration is not then tied to asingle reference value. Here, processing using Equation is preferable toEquation 26. Also, processing via Equation 25 may allow for an easierpre-patient procedure, since the custom target combines functions forboth fluorescence and reflectance system set-up, avoiding the need foran additional target test procedure.

[0279] Values of the custom target reflectance in a given individualinstrument 102 vary over time and as a function of wavelength. Forexample, FIG. 21 shows a graph 458 depicting as a function of wavelength462 a measure of the mean reflectivity 460, R_(cp), of the 10% diffusetarget 426 of FIG. 19 over the non-masked regions 445 shown in FIG. 20,obtained using the same instrument on two different days. R_(cp) iscalculated as shown in Equation 27:

R _(cp)(λ)=[

I _(cp)(i,λ,t _(o))

_(i)

/

I _(fc)(i,λ,t _(o))

_(i) ]·R _(fc)  (27)

[0280] where R_(fc)=0.6, the diffuse reflectance of the NIST-traceablestandard target. Values of R_(cp) vary as a function of wavelength 462,as seen in each of curves 464 and 466 of FIG. 21. Also, there is a shiftfrom curve 464 to curve 466, each obtained on a different day.Similarly, values of R_(cp) vary among different instrument units.Curves 464 and 466 show that R_(cp) varies with wavelength and variesfrom 0.1; thus, assuming R^(cp)=0.1 as in Equation 25 may introduceinaccuracy.

[0281] Equation 25 can be modified to account for this temporal andwavelength dependence, as shown in Equation 28:

R(i,λ,t′)=[I _(m)(i,λ,t′)/

I _(cp)(i,λ,t′)

_(i)

]·[

I _(fc)(i,λ,t _(o))

_(i) /I _(fc)(i,λ,t _(o))]·R _(cp,fitted)  (28)

[0282] where R_(cp,fitted) is an array of values of a second-orderpolynomial curve fit of R_(cp) shown in Equation 27. The polynomialcurve fit reduces the noise in the R_(cp) array. Other curve fits may beused alternatively. For example, FIG. 22A shows a graph 490 depicting,for seven individual instruments, curves 496, 498, 500, 502, 504, 506,508 of sample reflectance intensity using the BB1 white light source 188as depicted in FIGS. 4, 7 and 8 graphed as functions of wavelength 494.Each of the seven curves represents a mean of reflectance intensity ateach wavelength, calculated using Equation 25 for regions confirmed asmetaplasia by impression. FIG. 22B shows a graph 509 depictingcorresponding curves 510, 512, 514, 516, 518, 520, 522 of test samplereflectance intensity calculated using Equation 28, where R_(cp) varieswith time and wavelength. The variability between individual instrumentunits decreases when using measured values for R_(cp) as in Equation 28rather than as a constant value. The variability between reflectancespectra obtained from samples having a commontissue-class/state-of-health classification, but using differentinstrument units decreases when using measured values for R_(cp) as inEquation 28 rather than a constant value as in Equation 25.

[0283] In an alternative embodiment, processing of reflectance dataincludes applying Equation 28 without first fitting R_(cp) values to aquadratic polynomial. Thus, processing is performed in accordance withEquation 29 to adjust data from a test sample, I_(m), to produceadjusted reflectance R, as follows:

R(i,λ,t′)=[I _(m)(i,λ,t′)/

I _(cp)(i,λ,t′)

_(i)

]·[

I _(fc)(i,λ,t _(o))

_(i) /I _(fc)(i,λ,t _(o))]·R _(cp)  (29)

[0284] Applying Equation 29, however, introduces an inconsistency in thereflectance spectra at about 490 nm, caused, for example, by theintensity from the 60% reflectivity factory calibration target exceedingthe linear range of the CCD array. This can be avoided by using a darkerfactory calibration target in the factory NIST target test 314, forexample, a target having a known diffuse reflectance from about 10% toabout 30%.

[0285] Results from the factory/PM custom target test 312, thefactory/PM NIST target test 314, and the pre-patient custom target test330 provide the correction factors shown in blocks 324, 326, and 334,respectively used in preprocessing reflectance data from a patient scanusing the BB1 white light source 188 and the BB2 white light source 190shown in FIGS. 4, 7, and 8. Correction factors in block 324 representbackground-subtracted, power-monitor-corrected (power-corrected), andnull-target-subtracted reflectance data from a given factory/PM customtarget test 312 (cp) and are shown in Equations 30 and 31:

FCCTMMBB1=

I_(cp,BB1)(i,λ,t _(o))

_(i, masked) −FCNULLBB1  (30)

FCCTMMBB2=

I_(cp,BB2)(i,λ,t _(o))

_(i, masked) −FCNULLBB2  (31)

[0286] where FCNULLBB1 and FCNULLBB2 are given by Equations 4 and 5, and

_(i, masked) represents a one-dimensional array of mean data computed ona pixel-by-pixel basis in regions of area 445 of the scan pattern 444 ofFIG. 20.

[0287] Correction factors in block 326 of FIG. 10 represent ratios ofbackground-subtracted, power-corrected, and null-target-subtractedreflectance data from a factory/PM custom target test 312 (cp) and afactory/PM NIST standard target test 314 (fc) and are shown in Equations32, 33, and 34: $\begin{matrix}{{{FCBREF1}{\lbrack\rbrack}} = \frac{{\langle{{I_{{fc},{BB1}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\quad {of}\quad 4} - {FCNULLBB1}}\rangle}_{i,}}{{I_{{fc},{BB1}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\quad {of}\quad 4} - {FCNULLBB1}}} & (32) \\{{{FCBREF2}{\lbrack\rbrack}} = \frac{{\langle{{I_{{fc},{BB2}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\quad {of}\quad 4} - {FCNULLBB2}}\rangle}_{i,}}{{I_{{fc},{BB2}}\left( {i,\lambda,t_{o}} \right)}_{{avg}\quad {of}\quad 4} - {FCNULLBB2}}} & (33)\end{matrix}$

 CALREF=[0.5·(FCCTMBB1

/

FCBREF1[ ]

_(i))+(FCCTMBB2

/

FCBREF2[ ]

_(i))]_(interp,fit)

[0288] where values of the two-dimensional arrays I_(fc,BB1) andI_(fc,BB2) are averages of data using the target at each of fourpositions, rotated 90° between each position; and all divisions,subtractions, and multiplications are on a location-by-location,pixel-by-pixel basis. The correction factor, CALREF, is aone-dimensional array of values of the quantity in brackets [ ] on theright side of Equation 34, interpolated such that they correspond towavelengths at 1-nm intervals between λ=360 nm and λ=720 nm. Theinterpolated values are then fit with a quadratic or other polynomial toreduce noise.

[0289] Correction factors in block 334 of FIG. 10 representbackground-subtracted, power-corrected, internal-stray-light-correctedreflectance data from a pre-patient custom target test 330 (cp) and aregiven in Equations 35 and 36 as follows:

BREFMBB1

=

I _(cp,BB1)(i,λ,t′)−SLBB1

_(i)  (35)

BREFMBB2=

I_(cp,BB2)(i,λ,t′)−SLBB2

_(i)  (36)

[0290] where SLBB1 and SLBB2 are as shown in Equations 19 and 20.

[0291] Steps #4, 5, and 6 in each of blocks 342 and 344 of the spectraldata pre-processing block diagram 340 of FIG. 11 include processingpatient reflectance data using the correction factors from blocks 324,326, and 334 of FIG. 10 computed using results of the factory/PM customtarget test 312, the factory/PM NIST standard target test 314, and thepre-patient custom target test 330.

[0292] In step #4 of block 342 in FIG. 11, the array ofbackground-subtracted, power-corrected, internal-stray-light-subtractedpatient reflectance data obtained using the BB1 light source ismultiplied by the two-dimensional array correction factor, FCBREF1[ ],and then in step #5, is divided by the correction factor BREFMBB1. Afterfiltering using, for example, a 5-point median filter and a second-order27-point Savitsky-Golay filter, the resulting array is linearlyinterpolated using results of the wavelength calibration step 302 inFIG. 10 to produce a two-dimensional array of spectral datacorresponding to wavelengths ranging from 360 nm to 720 nm in 1-nmincrements at each of 499 interrogation points of the scan pattern 202shown in FIG. 5. This array is multiplied by CALREF in step #6 of block342 in FIG. 11, and pre-processing of the BB1 spectral data in thisembodiment is complete.

[0293] Steps #4, 5, and 6 in block 344 of FIG. 11 concern processing ofBB2 data and is directly analogous to the processing of BB1 datadiscussed above.

[0294] Steps #4 and 5 in block 346 of FIG. 1 include processingfluorescence data using factory/PM-level correction factors, appliedafter background correction (step #1), power monitor correction (step#2), and stray light correction (step #3) of fluorescence data from atest sample. Steps #4 and 5 include application of correction factorssFCDYE and IRESPONSE, which come from the factory/PM fluorescent dyecuvette test 306 and the factory/PM tungsten source test 308 in FIG. 10.

[0295] The factory/PM tungsten source test 308 accounts for thewavelength response of the collection optics for a given instrumentunit. The test uses a quartz tungsten halogen lamp as a light source.Emission from the tungsten filament approximates a blackbody emitter.Planck's radiation law describes the radiation emitted into a hemisphereby a blackbody (BB) emitter:

W _(BB)(λ)=[a·(CE)]/[λ⁵·{exp(b/λT)−1}]  (37)

[0296] where a=2πhc²=3.742×10¹⁶ [W(nm)⁴/cm²]; b=hc/k=1.439×10⁷ [(nm)K];T is source temperature; CE is a fitted parameter to account forcollection efficiency; and both T and CE are treated as variablesdetermined for a given tungsten lamp by curve-fitting emission data toEquation 37.

[0297] The lamp temperature, T, is determined by fitting NIST-traceablesource data to Equation 37. FIG. 23 shows a graph 582 depicting thespectral irradiance 584, W_(NIST lamp), of a NIST-traceablequartz-tungsten-halogen lamp, along with a curve fit 590 of the data tothe model in Equation 37 for blackbody irradiance, W_(BB). Since thelamp is a gray-body and not a perfect blackbody, Equation 37 includes aproportionality constant, CE. This proportionality constant alsoaccounts for the “collection efficiency” of the setup in an instrument102 as depicted in the tissue characterization system 100 of FIG. 1. Inthe illustrative embodiment, the target from which measurements areobtained is about 50-cm away from the lamp and has a finite collectioncone that subtends a portion of the emission hemisphere of the lamp.Thus, while W_(BB)(λ) in Equation 37 has units of [W/nm], calibrationvalues for a given lamp used in the instrument 102 in FIG. 1 has unitsof [W/cm²-nm at 50 cm distance]. The two calibration constants, CE andT, are obtained for a given lamp by measuring the intensity of the givenlamp relative to the intensity of a NIST-calibrated lamp using Equation38:

W _(lamp) =[I _(lamp) /I _(NIST lamp) ]·W _(NIST lamp)  (38)

[0298] Then, values of T and CE are determined by plotting W_(lamp)versus wavelength and curve-fitting using Equation 37. The curve fitprovides a calibrated lamp response, I_(lamp)(λ), to which the tungstenlamp response measured during factory/PM testing 308 at a giveninterrogation point and using a given instrument, S_(lamp)(i,λ), iscompared. This provides a measure of “instrument response”, IR(i,λ), forthe given point and the given instrument, as shown in Equation 39:

IR(i,λ)=S _(lamp)(i,λ)/I _(lamp)(λ)  (39)

[0299] The factory/PM tungsten source test 308 in FIG. 10 includescollecting an intensity signal from the tungsten lamp as its lightreflects off an approximately 99% reflective target. The test avoidsshadowing effects by alternately positioning the tungsten source at eachof two locations—for example, on either side of the probe head 192 atlocations corresponding to the white light source locations 188, 190shown in FIG. 8—and using the data for each given interrogation pointcorresponding to the source position where the given point is not inshadow.

[0300] Once the instrument response measure, IR(i,λ), is obtained, acorrection factor is determined such that its value is normalized tounity at a given wavelength, for example, at λ=500 nm. Thus, thedistance between the lamp and the detecting aperture, the photoelectronquantum efficiency of the detector, and the reflectivity of the targetdo not need to be measured.

[0301] According to the illustrative embodiment, the fluorescencecomponent of the spectral data pre-processing 114 of the system 100 ofFIG. 1 corrects a test fluorescence intensity signal, S_(F)(i,λ), forindividual instrument response by applying Equation 40 to produceI_(F)(i,λ), the instrument-response-corrected fluorescence signal:

I _(F)(i,λ)=S _(F)(i,λ)÷[{500·IR(i,λ)}/{λ·IR(i,500)}]  (40)

[0302] where IR(i,500) is the value of the instrument response measureIR at point i and at wavelength λ=500 nm; and where the term λ/500converts the fluorescence intensity from energetic to photometric units,proportional to fluorophore concentration. In one embodiment, thedifferences between values of IR at different interrogation points issmall, and a mean of IR(λ) over all interrogation points is used inplace of IR(i,λ) in Equation 40.

[0303] The fluorescent dye cuvette test 306 accounts for variations inthe efficiency of the collection optics 200 of a given instrument 102.Fluorescence collection efficiency depends on a number of factors,including the spectral response of the optics and detector used. In oneembodiment, for example, the collection efficiency tends to decreasewhen a scan approaches the edge of the optics. A fluorescent dye cuvettetest 306, performed as part of factory and/or preventive maintenance(PM) calibration, provides a means of accounting for efficiencydifferences.

[0304] An about 50-mm-diameter cuvette filled with a dye solution servesas a target for the fluorescent dye cuvette test 306 to account forcollection optic efficiency variation with interrogation point positionand variation between different units. The factory/PM dye-filled cuvettetest 306 includes obtaining the peak intensity of the fluorescenceintensity signal at each interrogation point of the dye-filled cuvette,placed in the calibration target port of the instrument 102, andcomparing it to a mean peak intensity of the dye calculated for aplurality of units.

[0305] Illustratively, a calibrated dye cuvette can be prepared asfollows. First, the fluorescence emission of a 10-mm-pathlength quartzcuvette filled with ethylene glycol is obtained. The ethylene glycol isof 99+% spectrophotometric quality, such as that provided by AldrichChemical Company. The fluorescence emission reading is verified to beless than about 3000 counts, particularly at wavelengths near the dyepeak intensity. An approximately 2.5×10⁻⁴ moles/L solution ofcoumarin-515 in ethylene glycol is prepared. Coumarin-515 is a powdereddye of molecular weight 347, produced, for example, by Exciton ChemicalCompany. The solution is diluted with ethylene glycol to a finalconcentration of about 1.2×10⁻⁵ moles/L. Then, a second 10-mm-pathlengthquartz cuvette is filled with the coumarin-515 solution, and an emissionspectrum is obtained. The fluorescence emission reading is verified tohave a maximum between about 210,000 counts and about 250,000 counts.The solution is titrated with either ethylene glycol or concentratedcourmarin-515 solution until the peak lies in this range. Once achieved,50-mm-diameter quartz cuvettes are filled with the titrated standardsolution and flame-sealed.

[0306] A correction factor for fluorescence collection efficiency can bedetermined as follows. First, the value of fluorescence intensity of aninstrument-response-corrected signal, I_(F)(i,λ), is normalized by ameasure of the UV light energy delivered to the tissue as in Equation41:

F _(T)(i,λ)=[I _(F)(i,λ)/P _(m)(i)]·[P _(m) /E _(μJ)]_(FC/PM)  (41)

[0307] where F_(T)(i,λ) is the instrument-response-corrected,power-monitor-corrected fluorescence intensity signal; P_(m)(i) is apower-monitor reading that serves as an indirect measure of laserenergy, determined by integrating or adding intensity readings frompixels on a CCD array corresponding to a portion on which a beam of theoutput laser light is directed; and [P_(m)/E_(μl)]_(FC/PM) is the ratioof power monitor reading to output laser energy determined duringfactory calibration and/or preventive maintenance (FC/PM).

[0308] Next, the illustrative embodiment includes obtaining thefluorescence intensity response of a specific unit at a specificinterrogation point (region) in its scan pattern using a cuvette of thetitrated coumarin-515 dye solution as the target, and comparing thatresponse to a mean fluorescence intensity response calculated for a setof units, after accounting for laser energy variations as in Equation41. Equation 42 shows a fluorescence collection efficiency correctionfactor for a given unit applied to an instrument-response-correctedfluorescence signal, I_(F)(i,λ), along with the energy correction ofEquation 41: $\begin{matrix}{{F_{T}\left( {i,\lambda} \right)} = {\frac{I_{F}\left( {i,\lambda} \right)}{P_{m}(i)} \cdot \left( \frac{P_{m}}{E_{\mu \quad J}} \right)_{PM} \cdot \left( \frac{{\langle{\frac{I_{Dye}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \cdot \frac{P_{m}}{E_{u\quad J}}}\rangle}_{Instruments}}{\frac{I_{Dye}\left( {i,\lambda_{p}} \right)}{P_{m}(i)} \cdot \frac{P_{m}}{E_{\mu \quad J}}} \right)_{PM}}} & (42)\end{matrix}$

[0309] where I_(Dye)(i,λ_(p)) is the peak measured fluorescenceintensity at interrogation position i using the dye-filled cuvette, asshown in FIG. 31; λ_(p) is the wavelength (or its approximate pixelindex equivalent) corresponding to the peak intensity; and the quantityin brackets

_(Instruments) is the mean power-corrected intensity at interrogationpoint 251, corresponding to the center of the exemplary scan pattern ofFIG. 5, calculated for a plurality of units.

[0310] The fluorescence collection efficiency tends to decrease when thescans approach the edge of the optics. FIG. 24 shows typicalfluorescence spectra from the dye test 306. The graph 614 in FIG. 24depicts as a function of wavelength 618 the fluorescence intensity 616of the dye solution at each region of a 499-point scan pattern. Thecurves 620 all have approximately the same peak wavelength, λ_(p), butthe maximum fluorescence intensity values vary.

[0311]FIG. 25 shows how the peak fluorescence intensity (intensitymeasured at pixel 131 corresponding approximately to λ_(p)) 624,determined in FIG. 24, varies as a function of scan position(interrogation point) 626. Oscillations are due at least in part tooptic scanning in the horizontal plane, while the lower frequency frownpattern is due to scan stepping in the vertical plane. According to theillustrative embodiment, curves of the fluorescence intensity of the dyecuvette at approximate peak wavelength are averaged to improve on thesignal-to-noise ratio.

[0312] Equation 42 simplifies to Equations 43 and 44 as follows:$\begin{matrix}{{F_{T}\left( {i,\lambda} \right)} = {\frac{I_{F}\left( {i,\lambda} \right)}{P_{m}(i)} \cdot \left( \frac{{\langle{\frac{I_{Dye}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \cdot \frac{P_{m}}{E_{u\quad J}}}\rangle}_{Instruments}}{\frac{I_{Dye}\left( {i,\lambda_{p}} \right)}{P_{m}(i)}} \right)_{PM}}} & (43) \\{\quad {= {\frac{I_{F}\left( {i,\lambda} \right)}{P_{m}(i)} \cdot {{FCDYE}(i)}}}} & (44)\end{matrix}$

[0313] The term, [P_(m)/E_(μl)]_(PM), drops out of equation 42.Variations in laser energy measurements become less important as theenergy is averaged over multiple measurements made on many instruments.

[0314] In FIG. 10, the correction factor sFCDYE in block 318 is aone-dimensional scalar array and is calculated using Equation 45:$\begin{matrix}{{sFCDYE} = {\cdot \left( \frac{{\langle{\frac{I_{Dye}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \cdot \frac{P_{m}}{E_{u\quad J}}}\rangle}_{Instruments}}{\frac{I_{Dye}\left( {i,\lambda_{p}} \right)}{P_{m}(i)}} \right)}} & (45)\end{matrix}$

[0315] Here, values of I_(Dye)(i,λ_(p)) are background-subtracted,power-corrected, and null-target-subtracted.

[0316] In FIG. 10, the correction factor IRESPONSE in block 320 is aone-dimensional array and is calculated using the results of thefactory/PM tungsten source test 308, as in Equation 46:

IRESPONSE=[{500·IR(i,λ)}/{λ·IR(i,500)}]  (46)

[0317] where IR(i,500) is the value of the instrument response measureIR given in Equation 39 at point i and at wavelength λ=500 nm.

[0318] Steps #4 and 5 in block 346 of the fluorescence spectral datapre-processing block diagram 340 of FIG. 11 include processingfluorescence data using sFCDYE and IRESPONSE as defined in Equations 45and 46. The fluorescence data pre-processing proceeds bybackground-subtracting, power-correcting, and stray-light-subtractingfluorescence data from a test sample using Bkgnd[ ], sPowerMonitor[ ],and SLFL as shown in Steps #1, 2, and 3 in block 346 of FIG. 11. Then,the result is multiplied by sFCDYE and divided by IRESPONSE on apixel-by-pixel, location-by-location basis. Next, the resultingtwo-dimensional array is smoothed using a 5-point median filter, then asecond-order, 27-point Savitsky-Golay filter, and interpolated using thepixel-to-wavelength conversion determined in block 302 of FIG. 10 toproduce an array of data corresponding to a spectrum covering a rangefrom 360 nm to 720 nm at 1-nm intervals, for each of 499 interrogationpoints of the scan pattern.

[0319] As a further feature, the stability of fluorescence intensityreadings are monitored between preventive maintenance procedures. Thismay be performed prior to each patient scan by measuring thefluorescence intensity of the center plug 438 of the custom target 426shown in FIG. 19 and comparing the result to the expected value from themost recent preventive maintenance test. If the variance from theexpected value is significant, and/or if the time between successivepreventive maintenance testing is greater than about a month, thefollowing correction factor may be added to those in block 346 of FIG.11: $\begin{matrix}{{FSTAB} = \frac{\left\lbrack \frac{I_{ct}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \right\rbrack_{PM}}{\left\lbrack \frac{I_{ct}\left( {251,\lambda_{p}} \right)}{P_{m}(251)} \right\rbrack_{PP}}} & (47)\end{matrix}$

[0320] where PM denotes preventive maintenance test results; PP denotespre-patient test results; I_(ct)(251, λ_(p)) is the fluorescence peakintensity reading at scan position 251 (center of the custom target) atpeak wavelength λ_(p); and P_(m) is the power monitor reading at scanposition 251.

[0321] The spectral data pre-processing 114 in FIG. 11 further includesa procedure for characterizing noise and/or applying a thresholdspecification for acceptable noise performance. Noise may be asignificant factor in fluorescence spectral data measurements,particularly where the peak fluorescence intensity is below about 20counts/μJ (here, and elsewhere in this specification, values expressedin terms of counts/μJ are interpretable in relation to the meanfluorescence of normal squamous tissue being 70 ct/μJ at about 450 nm).

[0322] The procedure for characterizing noise includes calculating apower spectrum for a null target background measurement. The null targetbackground measurement uses a null target having about 0% reflectivity,and the measurement is obtained with internal is lights off andoptionally with all external lights turned off so that room lights andother sources of stray light do not affect the measurement. Preferably,the procedure includes calculating a mean null target backgroundspectrum of the individual null target background spectra at allinterrogation points on the target—for example, at all 499 points of thescan pattern 202 of FIG. 5. Then, the procedure subtracts the meanspectrum from each of the individual null target background spectra andcalculates the Fast Fourier Transform (FFT) of each mean-subtractedspectrum. Then, a power spectrum is calculated for each FFT spectrum anda mean power spectrum is obtained.

[0323]FIG. 26 shows a graph 678 depicting exemplary mean power spectrafor various individual instruments 684, 686, 688, 690, 692, 694, 696. A27-point Savitzky-Golay filter has an approximate correspondingfrequency of about 6300 s⁻¹ and frequencies above about 20,000 s⁻¹ arerapidly damped by applying this filter. In the case of a 27-pointSavistzky-Golay filter, spectral data pre-processing in FIG. 11 furtherincludes applying a threshold maximum criterion of 1 count in the powerspectrum for frequencies below 20,000 s⁻¹. Here, data from an individualunit must not exhibit noise greater than 1 count at frequencies below20,000 s⁻¹ in order to satisfy the criterion. In FIG. 26, the criterionis not met for units with curves 692 and 696, since their power spectracontain points 706 and 708, each exceeding 1 count at frequencies below20,000 s⁻¹. The criterion is met for all other units.

[0324] According to an alternative illustrative embodiment, a secondnoise criterion is applied instead of or in addition to theaforementioned criterion. The second criterion specifies that the meanpower spectral intensity for a given unit be below 1.5 counts at allfrequencies. In FIG. 26, the criterion is not met for units with curves692 and 696, since their power spectra contain points 700 and 702, eachexceeding 1.5 counts.

[0325] The illustrative spectral data pre-processing 114 in FIG. 11and/or the factory/PM 110 and pre-patient calibration 116 and correctionin FIG. 10 further includes applying one or more validation criteria todata from the factory/PM 110 and pre-patient 114 calibration tests. TheValidation criteria identify possibly-corrupted calibration data so thatthe data are not incorporated in the core classifier algorithms and/orthe spectral masks of steps 132 and 130 in the system 100 of FIG. 1. Thevalidation criteria determine thresholds for acceptance of the resultsof the calibration tests. According to the illustrative embodiment, thesystem 100 of FIG. 1 signals if validation criteria are not met and/orprompts retaking of the data.

[0326] Validation includes validating the results of the factory/PM NIST60% diffuse reflectance target test 314 in FIG. 10. Validation may benecessary, for example, because the intensity of the xenon lamp used inthe test 314 oscillates during a scan over the 25-mm scan pattern 202 ofFIG. 5. The depth of modulation of measured reflected light intensitydepends, for example, on the homogeneity of the illumination source atthe target, as well as the collection efficiency over the scan field.The depth of modulation also depends on how well the target is alignedrelative to the optical axis. In general, inhomogeneities of theillumination source are less important than inhomogeneities due totarget misalignment, since illumination source inhomogeneities aregenerally accounted for by taking the ratio of reflected light intensityto incident light intensity. Thus, the calibration 110, 116 methods useone or two metrics to sense off-center targets and prompt retaking ofdata.

[0327] One such metric includes calculating a coefficient of variation,CV_(i)(λ), of measured reflected light intensity across the scan fieldaccording to Equation 48: $\begin{matrix}{{{CV}_{i}(\lambda)} = \frac{{std}\quad \left( {I\left( {\lambda,i} \right)} \right)_{i}}{{mean}\quad \left( {I\left( {\lambda,i} \right)} \right)_{i}}} & (48)\end{matrix}$

[0328] where I(λ,i)=mean[{I_(target)(λ,i)−I_(bkg)(λ,i)}/P_(m)(i)]_(4 rotations); “std”represents standard deviation; i represents an interrogation point; λrepresents wavelength (in one embodiment, between 370 nm and 700 nm);and P_(m)(i) represents the power monitor value for interrogation pointi. I(λ,i) is the mean of the background-subtracted (bkg),power-monitor-corrected reflectance intensity values from the NISTtarget measured 4 times, rotating the target 90° between eachmeasurement. Validation according to the metric of Equation 48 requiresthe value of CV_(i)(λ) be less than an experimentally-determined, fixedvalue.

[0329] Another metric from the 60% diffuse target test 314 includescalculating the relative difference, RD, between the minimum and maximummeasured intensity over the scan field according to Equation 49:$\begin{matrix}{{{{RD}(\lambda)} = {\frac{2 \cdot \left\lbrack {{\max \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i} - {\min \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i}} \right\rbrack}{\left\lbrack {{\max \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i} + {\min \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i}} \right\rbrack}\quad {where}}}{{I^{\prime}\left( {\lambda,i} \right)} = {{mean}\quad {\left( {\left( \frac{{I_{target}\left( {\lambda,i} \right)} - {I_{bkg}\left( {\lambda,i} \right)}}{P_{m}(i)} \right) \cdot {{mean}\left( {P_{m}(i)} \right)}_{i}} \right)_{4\quad {rotations}}.}}}} & (49)\end{matrix}$

[0330] by the mean of the power monitor values. In one embodiment, therelative difference, RD, between the minimum and maximum computed inEquation 49 is more sensitive to off-centered targets than thecoefficient of variation, CV_(i), computed in Equation 48. Here,validation requires the value of RD(λ) be less than anexperimentally-determined, fixed value. In the illustrative embodiment,validation requires that Equation 50 be satisfied as follows:

RD(λ)<0.7 for λ between 370 nm and 700 nm  (50)

[0331] where RD(λ) is given by Equation 49.

[0332] Validation also includes validating the results of the tungstensource test 308 from FIG. 11 using the approximately 99% diffusereflectivity target. This test includes obtaining two sets of data, eachset corresponding to a different position of the external tungstensource lamp. Data from each set that are not affected by shadow aremerged into one set of data. Since the power monitor correction is notapplicable for this external source, a separate background measurementis obtained.

[0333] The illustrative calibration methods 110, 116 use one or twometrics to validate data from the tungsten source test 308. One metricincludes calculating a coefficient of variation, CV_(i)(λ), of the meanforeground minus the mean background data, W(λ,i), of the merged set ofdata, as in Equation 51: $\begin{matrix}{{{CV}_{i}(\lambda)} = \frac{{std}\quad \left( {W\left( {\lambda,i} \right)} \right)_{i}}{{mean}\quad \left( {W\left( {\lambda,i} \right)} \right)_{i}}} & (51)\end{matrix}$

[0334] where the coefficient of variation, CV_(i)(λ), is calculatedusing the mean instrument spectral response curve, IR, averaging overall interrogation points of the scan pattern. Validation requires thevalue of CV_(i)(λ) be less than an experimentally-determined, fixedvalue. In the illustrative embodiment, validation requires that Equation52 be satisfied for all interrogation points i:

CV_(i)(λ)<0.5 for λ between 370 nm and 700 nm  (52)

[0335] where CV_(i)(λ) is given by Equation 51.

[0336] A second metric includes calculating a mean absolute differencespectrum, MAD(λ), comparing the current spectral response curve to thelast one measured, as in Equation 53:

MAD(λ)=mean(|IR _(t)(i,λ)−IR _(t−1)(i,λ)|)_(i)  (53)

[0337] where the instrument spectral response curve, IR, is given byEquation 39. Validation requires the value of MAD(λ) be less than anexperimentally-determined, fixed value. In one embodiment, validationrequires that Equation 54 be satisfied:

MAD(λ)<0.2 for λ between 370 nm and 700 nm  (54)

[0338] where MAD(λ) is given by Equation 53.

[0339] Validation can further include validating the results of thefluorescent dye cuvette test 306 in FIG. 10, used to standardizefluorescence measurements between individual units and to correcting forvariation in collection efficiency as a unit collects data atinterrogation points of a scan pattern. The illustrative calibrationmethods 110, 116 use one or more metrics to validate data from thefluorescent dye cuvette test 306 using a coefficient of variation,CV_(i)(λ), of dye cuvette intensity, I_(Dye), as in Equation 55:$\begin{matrix}{{{CV}_{i}(\lambda)} = \frac{{std}\quad \left( {I_{Dye}\left( {\lambda,i} \right)} \right)_{i}}{{mean}\quad \left( {I_{Dye}\left( {\lambda,i} \right)} \right)_{i}}} & (55)\end{matrix}$

[0340] The coefficient of variation, CV_(i)(λ), in Equation 55 betweenabout 470 nm and about 600 nm is generally representative offluorescence efficiency variations over the scan pattern. Thecoefficient of variation at about 674 nm is a measure of how well thecollection system blocks the 337-nm excitation light. As the excitationlight passes over the surface of the cuvette, the incidence andcollection angles go in and out of phase, causing modulation around 574nm. The coefficient of variation at about 425 nm is a measure of thecleanliness of the cuvette surface and is affected by the presence offingerprints, for example. The coefficient of variation below about 400nm and above about 700 nm is caused by a combination of the influence of337-nm stray excitation light and reduced signal-to-noise ratio due tolimited fluorescence from the dye solution at these wavelengths.

[0341] One metric includes calculating a mean coefficient of variation,CV_(i)(λ), according to Equation 55, between about 500 nm and about 550nm, and comparing the mean coefficient of variation to anexperimentally-determined, fixed value. According to the illustrativeembodiment, validation requires that Equation 56 be satisfied:

mean CV_(i)(λ)<0.06 for λ between 500 nm and 550 nm  (56)

[0342] A second metric includes requiring the coefficient of variationat about 674 nm be less than an experimentally-determined, fixed value.In one embodiment, validation requires that Equation 57 be satisfied forall interrogation points i:

CV _(i)(674)<0.5  (57)

[0343] where CV_(i)(λ) is calculated as in Equation 55.

[0344] Validation can also include validating results of the fluorescentdye cuvette test 306 using both Equations 56 and 57. Here, applyingEquation 56 prevents use of data from tests where the scan axis issignificantly shifted relative to the center of the optical axis, aswell as tests where the cuvette is not full or is off-center. ApplyingEquation 57 prevents use of data from tests where a faulty UV emissionfilter is installed or where the UV filter degrades over time, forexample.

[0345] Validation can also include validating the results of the 10%diffuse reflectivity custom target tests 312, 330 in FIG. 10. Here, anoff-center target may result in a faulty test due to interference atregions near the edge of the target, as well as regions near thefluorescent and phosphorescent plugs that are improperly masked.According to the illustrative embodiment, validation of the customtarget tests 312, 330 requires that the relative difference between theminimum and maximum intensity, RD(λ), is below a pre-determined value,where RD(λ) is calculated as in Equation 58: $\begin{matrix}{{{RD}(\lambda)} = \frac{2 \cdot \left\lbrack {{\max \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}} - {\min \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}}} \right\rbrack}{\left\lbrack {{\max \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}} + {\min \left( {I^{\prime}\left( {\lambda,i} \right)} \right)}_{i = {mask}}} \right\rbrack}} & (58)\end{matrix}$

[0346] where (I′(λ,i))_(i=mask) refers to all scan positions exceptthose masked to avoid the plugs, as shown in FIGS. 19 and 20. In oneembodiment, validation requires that Equation 59 be satisfied:

RD(λ)<1.2 for λ between 370 nm and 700 nm  (59)

[0347] where RD(λ) is calculated as in Equation 58.

[0348] The invention can also validate the results of the null targettest 304, 328 in FIG. 10. The null target test is used, for example, toaccount for internal stray light in a given instrument. According to theillustrative embodiment, a maximum allowable overall amount of straylight is imposed. For example, in one preferred embodiment, validationof a null target test 304, 328 requires the integrated energy, IE, bebelow a predetermined value, where IE is calculated frombackground-subtracted, power-monitor-corrected null target reflectanceintensity measurements, as in Equation 60: $\begin{matrix}{{IE} = {{\int_{870}^{700}{{{{mean}\left( \frac{{{null}\left( {\lambda,i} \right)} - {{bkg}\left( {\lambda,i} \right)}}{P_{m}(i)}\quad \right)}_{i} \cdot {{mean}\left( {P_{m}(i)} \right)}_{i}}{\lambda}}} \approx {\sum\limits_{370}^{700}\quad {{{mean}\left( \frac{{{null}\left( {\lambda,i} \right)} - {{bkg}\left( {\lambda,i} \right)}}{P_{m}(i)}\quad \right)}_{i} \cdot {{mean}\left( {P_{m}(i)} \right)}_{i}}}}} & (60)\end{matrix}$

[0349] where Δλ in the summation above is about 1-nm. In one embodiment,validation requires that Equation 61 be satisfied:

IE<4000 counts  (61)

[0350] where IE is calculated as in Equation 60.

[0351] The invention may also employ validation of the open air targettest 310 in FIG. 10. Like the null target test 304, 328, the open airtarget test is used in accounting for internal stray light in a giveninstrument. According to the illustrative embodiment, validation of anopen air target test 310 requires the integrated energy, IE, be below apredetermined value, where IE is calculated as in Equation 60, exceptusing open air reflectance intensity measurements in place of nulltarget measurements, null(λ,i). By way of example, in one casevalidation requires that the value of integrated energy for the open airtest be below 1.2 times the integrated energy from the null target test,calculated as in Equation 60.

[0352] According to another feature, the invention validates the powermonitor corrections used in the calibration tests in FIG. 10. Patientand calibration data that use a power monitor correction may beerroneous if the illumination source misfires. According to oneapproach, validation of a power monitor correction requires that themaximum raw power monitor intensity reading, P_(m,max)(i), be greaterthan a predetermined minimum value and/or be less than a predeterminedmaximum value at each interrogation point i. In the illustrativeembodiment, validation requires that Equation 62 be satisfied:

6000 counts<P_(m,max)(i)<30,000 counts for all i  (62)

[0353] According to the illustrative embodiment, spectral datapre-processing 114 in FIG. 1I includes accounting for the result of thereal-time motion tracker 106 in the system 100 of FIG. 1 when applyingthe correction factors in block diagram 340 of FIG. 11. As discussedherein, the system 100 of FIG. 1 applies the calibration-basedcorrections in FIG. 11 to spectral data acquired from a patient scan.These corrections are applied by matching spectral data from eachinterrogation point in a patient scan to calibration data from acorresponding interrogation point. However, a patient scan of the 499interrogation points shown in the scan pattern 202 of FIG. 5 takesapproximately 12 seconds. During those 12 seconds, it is possible thatthe tissue will shift slightly, due to patient movement. Thus, spectraldata obtained during a scan may not correspond to an initial indexlocation, since the tissue has moved from its original position inrelation to the scan pattern 202. The real-time motion tracker 106 ofFIG. 1 accounts for this movement by using data from video images of thetissue to calculate, as a function of scan time, a translational shiftin terms of an x-displacement and a y-displacement. The motion tracker106 also validates the result by determining whether the calculated x,ytranslational shift accurately accounts for movement of the tissue inrelation to the scan pattern or some other fixed standard such as theinitial position of component(s) of the data acquisition system (thecamera and/or spectroscope). The motion tracker 106 is discussed in moredetail below.

[0354] Illustratively, the spectral data pre-processing 114 in FIG. 11accounts for the result of the real-time motion tracker 106 by applyinga calibration spectra lookup method. The lookup method includesobtaining the motion-corrected x,y coordinates corresponding to theposition of the center of an interrogation point from which patientspectral data is obtained during a patient scan. Then the lookup methodincludes using the x,y coordinates to find the calibration data obtainedfrom an interrogation point whose center is closest to the x,ycoordinates.

[0355] The scan pattern 202 of FIG. 5 is a regular hexagonal samplinggrid with a pitch (center-to-center distance) of 1.1 mm and a maximuminterrogation point spot size of 1 mm. This center-to-center geometryindicates a horizontal pitch of 1.1 mm, a vertical pitch of about 0.9527mm, and a maximum corner distance of the circumscribed regular hexagonto the center of 0.635 mm. Thus, the illustrative lookup method findsthe calibration interrogation point whose center is closest to themotion-corrected x,y coordinates of a patient scan interrogation pointby finding coordinates of a calibration point that is less than 0.635 mmfrom x,y.

[0356] The background spectra, Bkgnd[ ], in FIG. 11, are obtained atnearly the same time patient spectral data are obtained and no motioncorrection factor is needed to background-subtract patient spectraldata. For example, at a given interrogation point during a patient scan,the system 100 of FIG. 1 pulses the UV light source on only whileobtaining fluorescence data, then pulses the BB1 light source on onlywhile obtaining the first set of reflectance data, then pulses the BB2light source on only while obtaining the second set of reflectance data,then obtains the background data, Bkgnd[ ], at the interrogation pointwith all internal light sources off. All of this data is considered tobe approximately simultaneous and no motion correction factor is neededfor the Bkgnd[ ] calibration data.

[0357] The real-time motion tracker 106 of FIG. 1 uses video dataobtained from the tissue contemporaneously with the spectral data. Inaddition to motion correction, the system of FIG. 1 uses video (image)data to determine image masks for disease probability computation, tofocus the probe 142 through which spectral and/or image data isacquired, and to compute a brightness and contrast correction and/orimage enhancement for use in disease overlay display.

Patient Scan Procedure

[0358]FIG. 27A is a block diagram 714 showing steps an operator performsbefore a patient scan as part of spectral data acquisition 104 in thesystem 100 of FIG. 1, according to an illustrative embodiment of theinvention. The steps in FIG. 27A are arranged sequentially with respectto a time axis 716. As shown, an operator applies a contrast agent tothe tissue sample 718, marks the time application is complete 720,focuses the probe 142 through which spectral and/or image data will beobtained 722, then initiates the spectral scan of the tissue 724 withina pre-determined window of time.

[0359] According to the illustrative embodiment, the window of time isan optimum range of time following application of contrast agent totissue within which an approximately 12 to 15 second scan can beperformed to obtain spectral data that are used to classify tissuesamples with a high degree of sensitivity and selectivity. The optimumwindow should be long enough to adequately allow for restarts indicatedby focusing problems or patient movement, but short enough so that thedata obtained is consistent. Consistency of test data is needed so thattissue classification results for the test data are accurate and so thatthe test data may be added to a bank of reference data used by thetissue classification scheme. In one illustrative embodiment, theoptimum window is expressed in terms of a fixed quantity of timefollowing application of contrast agent. In another illustrativeembodiment, the optimum window is expressed in terms of a threshold orrange of a trigger signal from the tissue, such as a reflectanceintensity indicative of degree of tissue whiteness.

[0360] The contrast agent in FIG. 27A is a solution of acetic acid.According to one exemplary embodiment, the contrast agent is a solutionbetween about 3 volume percent and about 6 volume percent acetic acid inwater. More particularly, in one preferred embodiment, the contrastagent is an about 5 volume percent solution of acetic acid in water.Other contrast agents may be used, including, for example, formic acid,propionic acid, butyric acid, Lugol's iodine, Shiller's iodine,methylene blue, toluidine blue, indigo carmine, indocyanine green,fluorescein, and combinations of these agents.

[0361] According to the illustrative embodiment, the time required toobtain results from a patient scan, following pre-patient calibrationprocedures, is a maximum of about 5 minutes. Thus, in FIG. 27A, thefive-minute-or-less procedure includes applying acetic acid to thetissue sample 726; focusing the probe (142) 728; waiting, if necessary,for the beginning of the optimum pre-determined window of time forobtaining spectral data 730; obtaining spectral data at allinterrogation points of the tissue sample 732; and processing the datausing a tissue classification scheme to obtain a diagnostic display 734.The display shows, for example, a reference image of the tissue samplewith an overlay indicating regions that are classified as necrotictissue, indeterminate regions, healthy tissue (no evidence of disease,NED), and CIN 2/3 tissue, thereby indicating where biopsy may be needed.

[0362] The times indicated in FIG. 27A may vary. For example, if thereal-time motion tracker 106 in the system of FIG. 1 indicates too muchmovement occurred during a scan 732, the scan 732 may be repeated ifthere is sufficient time left in the optimum window.

[0363]FIG. 27B is a block diagram 738 showing a time line for thespectral scan 732 indicated in FIG. 27A. In the embodiment shown in FIG.27B, a scan of all interrogation points of the scan pattern (forexample, the scan pattern 202 of FIG. 5) takes from about 12 seconds toabout 15 seconds, during which time a sequence of images is obtained formotion tracking, as performed in step 106 of the system 100 of FIG. 1.By the time a scan begins, a motion-tracking starting image 742 and atarget laser image 744 have been obtained 740. The target laser image744 may be used for purposes of off-line focus evaluation, for example.During the acquisition of spectral data during the scan, a frame grabber120 (FIG. 1) obtains a single image about once every second 746 for usein monitoring and/or correcting for movement of the tissue from oneframe to the next. In FIG. 27B, a frame grabber acquires images 748,750, 752, 754, 756, 758, 760, 762, 764, 766, 768 that are used to trackmotion that occurs during the scan.

[0364] Image data from a video subsystem is used, for example, in targetfocusing 728 in FIG. 27A and in motion tracking 106, 746 in FIG. 27B.Image data is also used in detecting the proper alignment of a target ina calibration procedure, as well as detecting whether a disposable is inplace prior to contact of the probe with a patient. Additionally, in oneembodiment, colposcopic video allows a user to monitor the tissue samplethroughout the procedure.

Video Calibration and Focusing

[0365]FIG. 28 is a block diagram 770 that shows the architecture of anillustrative video subsystem used in the system 100 of FIG. 1. FIG. 28shows elements of the video subsystem in relation to components of thesystem 100 of FIG. 1. The video subsystem 770 acquires single videoimages and real-time (streaming) video images. The video subsystem 770can post-process acquired image data by applying a mask overlay and/orby adding other graphical annotations to the acquired image data.Illustratively, image data is acquired in two frame buffers duringreal-time video acquisition so that data acquisition and data processingcan be alternated between buffers. The camera(s) 772 in the videosubsystem 770 of FIG. 28 include a camera located in or near the probehead 192 shown in FIG. 4, and optionally includes a colposcope cameraexternal to the probe 142 for visual monitoring of the tissue sampleduring testing. In one illustrative embodiment, only the probe headcamera is used. FIG. 28 shows a hardware interface 774 between thecameras 772 and the rest of the video subsystem 770. The frame grabber120 shown in FIG. 1 acquires video data for processing in othercomponents of the tissue characterization system 100. In one embodiment,the frame grabber 120 uses a card for video data digitization (videocapture) and a card for broadband illumination (for example, flashlamps) control. For example, one embodiment uses a Matrox Meteor 2 cardfor digitization and an Imagenation PXC-200F card for illuminationcontrol, as shown in block 776 of FIG. 28.

[0366] Real-time (streaming) video images are used for focusing theprobe optics 778 as well as for visual colposcopic monitoring of thepatient 780. Single video images provide data for calibration 782,motion tracking 784, image mask computation (used in tissueclassification) 786, and, optionally, detection of the presence of adisposable 788. In some illustrative embodiments, a single referencevideo image of the tissue sample is used to compute the image masks 108in the system 100 of FIG. 1. This reference image is also used indetermining a brightness and contrast correction and/or other visualenhancement 126, and is used in the disease overlay display 138 in FIG.1.

[0367] The illustrative video subsystem 770 acquires video data 790 froma single video image within about 0.5 seconds. The video subsystem 770acquires single images in 24-bit RGB format and is able to convert themto grayscale images. For example, image mask computation 108 in FIG. 1converts the RGB color triplet data into a single luminance value, Y,(grayscale intensity value) at each pixel, where Y is given by Equation63:

Y=0.299R+0.587G+0.114B  (63)

[0368] where the grayscale intensity component, Y, is expressed in termsof red (R), green (G), and blue (B) intensities; and where R, G, and Brange from 0 to 255 for a 24-bit RGB image.

[0369] Laser target focusing 728 is part of the scan procedure in FIG.27A. An operator uses a targeting laser in conjunction with real-timevideo to quickly align and focus the probe 142 prior to starting apatient scan. In the illustrative embodiment, an operator performs alaser “spot” focusing procedure in step 728 of FIG. 27A where theoperator adjusts the probe 142 to align laser spots projected onto thetissue sample. The user adjusts the probe while looking at a viewfinderwith an overlay indicating the proper position of the laser spots. Inone alternative embodiment, an operator instead performs a thin-linelaser focusing method, where the operator adjusts the probe until thelaser lines become sufficiently thin. The spot focus method allows forfaster, more accurate focusing than a line-width-based focusingprocedure, since thin laser lines can be difficult to detect on tissue,particularly dark tissue or tissue obscured by blood. Quick focusing isneeded in order to obtain a scan within the optimal time windowfollowing application of contrast agent to the tissue; thus, aspot-based laser focusing method is preferable to a thin line method,although a thin line focus method may be used in alternativeembodiments.

[0370] A target focus validation procedure 122 is part of the tissuecharacterization system 100 of FIG. 1, and determines whether theoptical system of the instrument 102 is in focus prior to a patientscan. If the system is not in proper focus, the acquired fluorescenceand reflectance spectra may be erroneous. Achieving proper focus isimportant to the integrity of the image masking 108, real-time tracking106, and overall tissue classification 132 components of the system 100of FIG. 1.

[0371] The focus system includes one or more target laser(s) thatproject laser light onto the patient sample prior to a scan. In oneembodiment, the targeting laser(s) project laser light from the probehead 192 toward the sample at a slight angle with respect to the opticalaxis of the probe 142 so that the laser light that strikes the samplemoves within the image frame when the probe is moved with respect to thefocal plane. For example, in one illustrative embodiment, four laserspots are directed onto a target such that when the probe 142 movestoward the target during focusing, the spots move closer together,toward the center of the image. Similarly, when the probe 142 moves awayfrom the target, the spots move further apart within the image frame,toward the corners of the image.

[0372]FIG. 29A is a single video image 794 of a target 796 of 10%diffuse reflectivity upon which a target laser projects a focusingpattern of four laser spots 798, 800, 802, 804. During laser targetfocusing 728 (FIG. 27A), an operator views four focus rings that aredisplayed at predetermined locations, superimposed on the targetfocusing image. FIG. 29B depicts the focusing image 794 on the target796 in FIG. 29A with superimposed focus rings 806, 808, 810, 812. Theoperator visually examines the relative positions of the laser spots798, 800, 802, 804 in relation to the corresponding focus rings 806,808, 810, 812 while moving the probe head 192 along the optical axistoward or away from the target/tissue sample. When the laser spots liewithin the focus rings as shown in FIG. 29B, the system is within itsrequired focus range. The best focus is achieved by aligning the centersof all the laser spots with the corresponding centers of the focusrings. Alternatively, spot patterns of one, two, three, five, or morelaser spots may be used for focus alignment.

[0373] It is generally more difficult to align laser spots that strike anon-flat tissue sample target than to align the spots on a flat, uniformtarget as shown in FIG. 29B. In some instances, a laser spot projectedonto tissue is unclear, indistinct, or invisible. Visual evaluation offocus may be subjective and qualitative. Thus, a target focus validationprocedure is useful to insure proper focus of a tissue target isachieved. Proper focus allows the comparison of both image data andspectral data from different instrument units and different operators.

[0374] In one illustrative embodiment, the system 100 of FIG. 1 performsan automatic target focus validation procedure using a single focusimage. The focus image is a 24-bit RGB color image that is obtainedbefore acquisition of spectral data in a patient scan. The focus imageis obtained with the targeting laser turned on and the broadband lights(white lights) turned off. Automatic target focus validation includesdetecting the locations of the centers of visible laser spots andmeasuring their positions relative to stored, calibrated positions(“nominal” center positions). Then, the validation procedure applies adecision rule based on the number of visible laser spots and theirpositions and decides whether the system is in focus and a spectral scancan be started.

[0375]FIG. 30 is a block diagram 816 of a target focus validationprocedure according to an illustrative embodiment of the invention. Thesteps include obtaining a 24-bit RGB focus image 818, performing imageenhancement 820 to highlight the coloration of the laser spots,performing morphological image processing (dilation) to fill holes andgaps within the spots 822, defining a region of interest (ROI) of theimage 824, and computing a mean and standard deviation 826 of theluminance values (brightness) of pixels within the region of interest.Next, the focus validation procedure iteratively and dynamicallythresholds 828 the enhanced focus image using the computed mean andstandard deviation to extract the laser spots. Between thresholdingiterations, morphological processing 830 disconnects differentiatedimage objects and removes small image objects from the thresholdedbinary image, while a region analysis procedure 832 identifies andremoves image objects located outside the bounds of the target laserspot pathways 838 and objects whose size and/or shape do not correspondto a target laser spot. After all thresholding iterations, the found“spots” are either verified as true target laser spots or are removedfrom the image 834, based on size, shape, and/or location. Next, in step842, the focus validation procedure computes how far the centers of thefound spots are from the nominal focus centers and converts thedifference from pixels to millimeters in step 844. The validationprocedure then applies a decision rule based on the number of foundspots and their positions and decides whether the system is in focussuch that a spectral scan of the patient can begin.

[0376] The focus validation procedure of FIG. 30 begins with obtainingthe 24-bit RGB focus image and splitting it into R, G, and B channels.Each channel has a value in the range of 0 to 255. FIG. 31 depicts theRGB focus image 794 from FIG. 29A with certain illustrative geometrysuperimposed. FIG. 31 shows the four nominal spot focus centers 850,852, 854, 856 as red dots, one of which is the red dot labeled “N” inquadrant 1. The nominal spot focus centers represent the ideal locationof centers of the projected laser spots, achieved when the probe opticsare in optimum focus. The nominal spot focus centers 850, 852, 854, 856correspond to the centers of the rings 806, 808, 810, 812 in FIG. 29B.An (x,y) position is determined for each nominal focus center. A nominalimage focus center (857), O, is defined by the intersection of the twored diagonal lines 858, 860 in FIG. 31. The red diagonal lines 858, 860connect the two pairs of nominal spot focus centers 852, 854 inquadrants 2 and 3 and 850, 856 in quadrants 1 and 4, respectively. Also,the slopes of the two lines 858, 860 are computed for later use.

[0377] Step 820 in the procedure of FIG. 30 is image enhancement tohighlight the coloration of the laser spots in contrast to thesurrounding tissue. In one embodiment, the R value of saturated spots is“red clipped” such that if R is greater than 180 at any pixel, the Rvalue is reduced by 50. Then, a measure of greenness, G_(E), of eachpixel is computed as in Equation 64:

G _(E) =G−R−15  (64)

[0378] where G is the green value of a pixel, R is the red value of thepixel, and 15 is a correction factor to remove low intensity noise,experimentally-determined here to be 15 gray levels.

[0379]FIG. 32A represents the green channel of an RGB image 864 of acervical tissue sample, used in an exemplary target focus validationprocedure. In this image, only two top focus laser spots 868, 870 areclear. The lower right spot 872 is blurred/diffused while the lower leftspot 874 is obscured. The green-channel luminance (brightness), G_(E),of the green-enhanced RGB image 864 of FIG. 32A may be computed usingEquation 64 and may be displayed, for example, as grayscale luminancevalues between 0 and 255 at each pixel.

[0380] In step 822 of FIG. 30, the focus validation procedure performsmorphological dilation using a 3×3 square structuring element to fillholes and gaps within the found spots. Then in step 824, the procedureuses a pre-defined, circular region of interest (ROI) for computing amean, M, and a standard deviation, STD, 826 of the greenness value,G_(E), of the pixels within the ROI, which are used in iterative dynamicthresholding 828. According to the illustrative embodiment, the ROI is asubstantially circular region with a 460-pixel diameter whose centercoincides with the nominal image focus center, O.

[0381] Before iterative dynamic thresholding begins, G_(E) is set equalto zero at a 50-pixel diameter border about the ROI. Then, iterativedynamic thresholding 828 begins by setting an iteration variable, p, tozero, then computing a threshold value, Th, as follows:

Th=M+p·STD  (65)

[0382] where M and STD are defined from the ROI. Since p=0 in the firstiteration, the threshold, Th, is a “mean” greenness value over theentire ROI in the first iteration. In this embodiment, imagethresholding is a subclass of image segmentation that divides an imageinto two segments. The result is a binary image made up of pixels, eachpixel having a value of either 0 (off) or 1 (on). In step 828 of thefocus validation procedure of FIG. 30, the enhanced greenness value of apixel corresponding to point (x,y), within the ROI, G_(E)(X,Y), iscompared to the threshold value, Th. The threshold is applied as inEquation 66:

IF G _(E)(x,y)>Th, THEN the binary pixel value at (x,y), B_(T=)1, elseB_(T)=0.  (66)

[0383] Iterative dynamic thresholding 828 proceeds by performingmorphological opening 830 to separate nearby distinguishable imageobjects and to remove small objects of the newly thresholded binaryimage. According to the illustrative embodiment, the morphologicalopening 830 includes performing an erosion, followed by a dilation, eachusing a 3×3 square structuring element. The procedure then determinesthe centroid of each of the thresholded objects and removes each objectwhose center is outside the diagonal bands bounded by two lines that are40 pixels above and below the diagonal lines 858, 860 in FIG. 31. Thesediagonal bands include the region between lines 876, 878 and the regionbetween lines 880, 882 in FIG. 31, determined in step 838 of FIG. 30. Animage object whose center lies outside these bands does not correspondto a target focus spot, since the centers of the focus laser spotsshould appear within these bands at any position of the probe along theoptical axis. The spots move closer together, within the bands, as theprobe moves closer to the tissue sample, and the spots move fartherapart, within the bands, as the probe moves away from the tissue sample.

[0384] Next, step 832 of the thresholding iteration 828 computes an area(A), eccentricity (E), and equivalent diameter (ED) of the found imageobjects, and removes an object whose size and/or shape—described here byA, E, and ED—does not correspond to that of a focus laser spot. E and EDare defined as follows:

E=(1−b ² /a ²)^(0.5)  (67)

ED=2(A/π)^(0.5)  (68)

[0385] where a is the minor axis length and b is the major axis lengthin units of pixels. For example, step 832 applies Equation 69 asfollows:

IF A>5000 OR IF E>0.99 OR IF ED>110, THEN remove object (set B_(T)=0 forall pixels in object).  (69)

[0386] Other criteria may be applied. For example, Equation 70 may beapplied in place of Equation 69:

IF A>2500 OR IF E>0.99 OR IF ED>80, THEN remove object (set B_(T)=0 forall pixels in object).  (70)

[0387] Next, the iteration variable, p, is increased by a fixed value,for example, by 0.8, and a new threshold is calculated using Equation65. The iteration proceeds by applying the new threshold, performing amorphological opening, computing centroids of the newly thresholdedregions, removing regions whose center position, size, and/or shape donot correspond to those of a target focus spot, and stepping up thevalue of the iteration variable p. Iterative dynamic thresholdingproceeds until a pre-determined condition is satisfied. For example, thethresholding ends when the following condition is satisfied:

IF p>6 OR IF the number of qualified spots (image objects)≦4, THENSTOP.  (71)

[0388] Step 834 of the focus validation procedure eliminates any imageobject remaining after dynamic thresholding that does not meet certainlaser spot size and shape criteria. For example, according to theillustrative embodiment, step 834 applies the condition in Equation 72for each remaining image object:

IF A<80 OR IF E>0.85 OR IF ED<10, THEN remove object.  (72)

[0389] In an alternative embodiment, one or more additional criteriabased on the position of each image object (found spot) are applied toeliminate objects that are still within the focus bands of FIG. 31, butare too far from the nominal centers 850, 852, 854, 856 to be validfocus spots.

[0390]FIG. 32B shows an image 898 of the cervical tissue sample of FIG.32A following step 834, wherein the top two image objects were verifiedas target laser spots, while the bottom objects were eliminated.

[0391] Step 842 of the focus validation procedure assigns each of thefound spots to its respective quadrant and computes the centroid of eachfound spot. FIG. 31 shows the found spots as blue dots 900, 902, 904,906. Then for each found spot, step 842 computes the distance betweenthe center of the spot to the nominal image focus center 857, O. For thefocus spot center 900 labeled “F” in FIG. 31, this distance is L_(OF),the length of the blue line 910 from point O to point F. The distancebetween the nominal focus center, N, 850 corresponding to the quadrantcontaining the found spot, and the nominal image focus center 857, O, isL_(ON), the length of the red line 912 from point O to point N. Step 842of the focus validation procedure then determines a focus value forverified focus spot 900 equal to the difference between the lengthsL_(OF) and L_(ON). The focus value of each of the verified focus spotsis computed in this manner, and the focus values are converted frompixels to millimeters along the focus axis (z-axis) in step 844 of FIG.30 using an empirically-determined conversion ratio—for example, 0.34 mmper pixel.

[0392] Next, the focus validation procedure of FIG. 30 applies adecision rule in step 846 based on the number of found spots and theirpositions. The decision rule is a quantitative means of deciding whetherthe system is in focus and a spectral scan of the tissue can begin.According to the illustrative embodiment, step 846 applies a decisionrule given by Equations 73, 74, and 75:

IF 3 or more spots are found, THEN IF the focus value determined in step842 is ≦6 mm for any 3 spots OR IF the focus value is ≦4 mm for any 2spots, THEN “Pass”, ELSE “Fail” (require refocus).  (73)

IF only 2 spots are found, THEN IF the focus value of any spot is ≧4 mm,THEN “Fail” (require refocus), ELSE “Pass”.  (74)

IF≦1 spot is found, THEN “Fail” (require refocus).  (75)

[0393] Other decision rules may be used alternatively.

[0394]FIGS. 33 and 34 show the application of the focus validationprocedure of FIG. 30 using a rubber cervix model placed so that the twoupper laser spots are within the os region. For this example, thedistance between the edge of the probe head 192 and the target (ortarget tissue) is approximately 100 mm at optimum focus, and thedistance light travels between the target (or target tissue) and thefirst optic within the probe 142 is approximately 130 mm at optimumfocus.

[0395]FIG. 33 is a 24-bit RGB target laser focus image 942 of a rubbercervix model 944 onto which four laser spots 946, 948, 950, 952 areprojected. The cervix model 944 is off-center in the image 942 such thatthe two upper laser spots 946, 948 lie within the region. FIG. 34 showsa graph 954 depicting as a function of probe position relative to thetarget tissue 956, the mean of a focus value 958 (in pixels) of each ofthe four laser spots 946, 948, 950, 952 projected onto the rubber cervixmodel 944. The curve fit 960 of the data indicates the relationshipbetween measured focus, f, 958 and probe location, zp, 956 (in mm) issubstantially linear. However, the curve is shifted down and is notcentered at (0,0). This indicates a focus error introduced by the manualalignment used to obtain the z=0 focus position. Such an error mayprompt a “Fail” determination in step 846 of the focus validationprocedure of FIG. 30, depending on the chosen decision rule. FIG. 34indicates the difficulty in making a visual focus judgment to balancethe focus of the four spots, particularly where the target surface(tissue sample) is not flat and perpendicular to the optical axis(z-axis) of the probe system.

[0396] The focus validation procedure illustrated in FIG. 30 provides anautomatic, quantitative check of the quality of focus. Additionally, inthe illustrative embodiment, the focus validation procedure predicts theposition of optimum focus and/or automatically focuses the opticalsystem accordingly by, for example, triggering a galvanometer subsystemto move the probe to the predicted position of optimum focus.

[0397] The focus validation procedure in FIG. 30 produces a finaldecision in step 846 of “Pass” or “Fail” for a given focus image, basedon the decision rule given by Equations 73-75. This indicates whetherthe focus achieved for this tissue sample is satisfactory and whether aspectral data scan may proceed as shown in step 732 of FIGS. 27A and27B.

Determining Optimal Data Acquisition Window

[0398] After application of contrast agent 726 and target focusing 728,step 730 of FIG. 27A indicates that the operator waits for the beginningof the optimum window for obtaining spectral data unless the elapsedtime already exceeds the start of the window. The optimum windowindicates the best time period for obtaining spectral data, followingapplication of contrast agent to the tissue, considering the generaltime constraints of the entire scan process in a given embodiment. Forexample, according to the illustrative embodiment, it takes from about12 to about 15 seconds to perform a spectral scan of 499 interrogationpoints of a tissue sample. An optimum window is determined such thatdata may be obtained over a span of time within this window from asufficient number of tissue regions to provide an adequately detailedindication of disease state with sufficient sensitivity and selectivity.The optimum window preferably, also allows the test data to be used, inturn, as reference data in a subsequently developed tissueclassification module. According to another feature, the optimum windowis wide enough to allow for restarts necessitated, for example, byfocusing problems or patient movement. Data obtained within the optimumwindow can be added to a bank of reference data used by a tissueclassification scheme, such as component 132 of the system 100 ofFIG. 1. Thus, the optimum window is preferably narrow enough so thatdata from a given region is sufficiently consistent regardless of when,within the optimum window, it is obtained.

[0399] According to the illustrative embodiment, the optimal window forobtaining spectral data in step 104 of FIG. 1 is a period of time fromabout 30 seconds following application of the contrast agent to about130 seconds following application of the contrast agent. The time ittakes an operator to apply contrast agent to the tissue sample may vary,but is preferably between about 5 seconds and about 10 seconds. Theoperator creates a time stamp in the illustrative scan procedure of FIG.27A after completing application of the contrast agent, and then waits30 seconds before a scan may begin, where the optimum window is betweenabout 30 seconds and about 130 seconds following application of contrastagent. If the scan takes from about 12 seconds to about 15 seconds tocomplete (where no retake is required), the start of the scan proceduremust begin soon enough to allow all the data to be obtained within theoptimum window. In other words, in this embodiment, the scan must beginat least before 115 (assuming a worst case of 15 seconds to complete thescan) seconds following the time stamp (115 seconds after application ofcontrast agent) so that the scan is completed by 130 seconds followingapplication of contrast agent. Other optimum windows may be used. In oneembodiment, the optimum window is between about 30 seconds and about 110seconds following application of contrast agent. One alternativeembodiment has an optimal window with a “start” time from about 10 toabout 60 seconds following application of acetic acid, and an “end” timefrom about 110 to about 180 seconds following application of aceticacid. Other optimum windows may be used.

[0400] In one illustrative embodiment, the tissue characterizationsystem 100 of FIG. 1 includes identifying an optimal window for a givenapplication, and/or subsequently using spectral data obtained within thepre-determined window in a tissue classification module, such as step132 of FIG. 1. According to one feature, optimal windows are determinedby obtaining optical signals from reference tissue samples with knownstates of health at various times following application of a contrastagent.

[0401] Determining an optimal window illustratively includes the stepsof obtaining a first set of optical signals from tissue samples having aknown disease state, such as CIN 2/3 (grades 2 and/or 3 cervicalintraepithelial neoplasia); obtaining a second set of optical signalsfrom tissue samples having a different state of health, such as non-CIN2/3; and categorizing each optical signal into “bins” according to thetime it was obtained in relation to the time of application of contrastagent. The optical signal may include, for example, a reflectancespectrum, a fluorescence spectrum, a video image intensity signal, orany combination of these.

[0402] A measure of the difference between the optical signalsassociated with the two types of tissue is then obtained, for example,by determining a mean signal as a function of wavelength for each of thetwo types of tissue samples for each time bin, and using adiscrimination function to determine a weighted measure of differencebetween the two mean optical signals obtained within a given time bin.This provides a measure of the difference between the mean opticalsignals of the two categories of tissue samples—diseased andhealthy—weighted by the variance between optical signals of sampleswithin each of the two categories.

[0403] According to the illustrative embodiment, the invention furtherincludes developing a classification model for each time bin for thepurpose of determining an optimal window for obtaining spectral data instep 104 of FIG. 1. After determining a measure of difference betweenthe tissue types in each bin, an optimal window of time fordifferentiating between tissue types is determined by identifying atleast one bin in which the measure of difference between the two tissuetypes is substantially maximized. For example, an optimal window of timemay be chosen to include every time bin in which a respectiveclassification model provides an accuracy of 70% or greater. Here, theoptimal window describes a period of time following application of acontrast agent in which an optical signal can be obtained for purposesof classifying the state of health of the tissue sample with an accuracyof at least 70%. Models distinguishing between three or more categoriesof tissue may also be used in determining an optimal window forobtaining spectral data. As discussed below, other factors may also beconsidered in determining the optimal window.

[0404] An analogous embodiment includes determining an optimal thresholdor range of a measure of change of an optical signal to use in obtaining(or triggering the acquisition of) the same or a different signal forpredicting the state of health of the sample. Instead of determining aspecific, fixed window of time, this embodiment includes determining anoptimal threshold of change in a signal, such as a video image whitenessintensity signal, after which an optical signal, such as a diffusereflectance spectrum and/or a fluorescence spectrum, can be obtained toaccurately characterize the state of health or other characteristic ofthe sample. This illustrative embodiment includes monitoring reflectanceand/or fluorescence at a single or multiple wavelength(s), and uponreaching a threshold change from the initial condition, obtaining a fullreflectance and/or fluorescence spectrum for use in diagnosing theregion of tissue. This method allows for reduced data retrieval andmonitoring, since it involves continuous tracking of a single,partial-spectrum or discrete-wavelength “trigger” signal (instead ofmultiple, full-spectrum scans), followed by the acquisition of spectraldata in a spectral scan for use in tissue characterization, for example,the tissue classification module 132 of FIG. 1. Alternatively, thetrigger may include more than one discrete-wavelength orpartial-spectrum signal. The measure of change used to trigger obtainingone or more optical signals for tissue classification may be a weightedmeasure, and/or it may be a combination of measures of change of morethan one signal.

[0405] In a further illustrative embodiment, instead of determining anoptimal threshold or range of a measure of change of an optical signal,an optimal threshold or range of a measure of the rate of change of anoptical signal is determined. For example, the rate of change ofreflectance and/or fluorescence is monitored at a single or multiplewavelength(s), and upon reaching a threshold rate of change, a spectralscan is performed to provide spectral data for use in diagnosing theregion of tissue. The measure of rate of change used to triggerobtaining one or more optical signals for tissue classification may be aweighted measure, and/or it may be a combination of measures of changeof more than one signal. For example, the measured rate of change may beweighted by an initial signal intensity.

[0406] According to the illustrative embodiment, the optimum time windowincludes a time window in which spectra from cervical tissue may beobtained such that sites indicative of grades 2 and 3 cervicalintraepithelial neoplasia (CIN 2/3) can be separated from non-CIN 2/3sites. Non-CIN 2/3 sites include sites with grade 1 cervicalintraepithelial neoplasia (CIN 1), as well as NED sites, normal columnarand normal squamous epithelia, and mature and immature metaplasia.Alternately, sites indicative of high grade disease, CIN 2+, whichincludes CIN 2/3 categories, carcinoma in situ (CIS), and cancer, may beseparated from non-high-grade-disease sites. In general, for anyembodiment discussed herein in which CIN 2/3 is used as a category forclassification or characterization of tissue, the more expansivecategory CIN 2+ may be used alternatively. Preferably, the system 100can differentiate amongst three or more classification categories.Exemplary embodiments are described below and include analysis of thetime response of diffuse reflectance and/or 337-nm fluorescence spectraof a set of reference tissue samples with regions having known states ofhealth to determine temporal characteristics indicative of therespective states of health. These characteristics are then used inbuilding a model to determine a state of health of an unknown tissuesample. Other illustrative embodiments include analysis of fluorescencespectra using other excitation wavelengths, such as 380 nm and 460 nm,for example.

[0407] According to one illustrative embodiment, an optimum window isdetermined by tracking the difference between spectral data of twotissue types using a discrimination function.

[0408] According to the illustrative embodiment, the discriminationfunction shown below in Equation 76 may be used to extract differencesbetween tissue types: $\begin{matrix}{{D(\lambda)} = \frac{{\mu \left( {{test}(\lambda)} \right)}_{{non} - {{CIN}\quad {2/3}}} - {\mu \left( {{test}(\lambda)} \right)}_{{CIN}\quad {2/3}}}{\sqrt{{\sigma^{2}\left( {{test}(\lambda)} \right)}_{{non} - {{CIN}\quad {2/3}}} + {\sigma^{2}\left( {{test}(\lambda)} \right)}_{{CIN}\quad {2/3}}}}} & (76)\end{matrix}$

[0409] where μ corresponds to the mean optical signal for the tissuetype indicated in the subscript; and a corresponds to the standarddeviation. The categories CIN 2/3 and non-CIN 2/3 are used in thisembodiment because spectral data is particularly well-suited fordifferentiating between these two categories of tissue, and becausespectral data is prominently used in one embodiment of theclassification schema in the tissue classification module in step 132 ofFIG. 1 to identify CIN 2/3 tissue. Thus, in this way, it is possible totailor the choice of an optimal scan window such that spectral dataobtained within that window are well-adapted for use in identifying CIN2/3 tissue in the tissue classification scheme 132. In one illustrativeembodiment, the optical signal in Equation 76 includes diffusereflectance. In another illustrative embodiment, the optical signalincludes 337-nm fluorescence emission spectra. Other illustrativeembodiments use fluorescence emission spectra at another excitationwavelength such as 380 nm and 460 nm. In still other illustrativeembodiments, the optical signal is a video signal, Raman signal, orinfrared signal. Some illustrative embodiments include using differencespectra calculated between different phases of acetowhitening, usingvarious normalization schema, and/or using various combinations ofspectral data and/or image data as discussed above.

[0410] In one preferred embodiment, determining an optimal window forobtaining spectral data in step 104 of FIG. 1 includes developing lineardiscriminant analysis models using spectra from each time bin shown inTable 1 below. TABLE 1 Time bins for which means spectra are obtained inan exemplary embodiment Bin Time after application of Acetic Acid (s) 1t ≦ 0 2  0 < t ≦ 40 3 40 < t ≦ 60 4 60 < t ≦ 80 5  80 < t ≦ 100 6 100 <t ≦ 120 7 120 < t ≦ 140 8 140 < t ≦ 160 9 160 < t ≦ 180 10 t > 180

[0411] Alternatively, nonlinear discriminant analysis models may bedeveloped. Generally, models for the determination of an optimal windoware trained using reflectance and fluorescence data separately, althoughsome embodiments include using both data types to train a model. Thediscriminant analysis models discussed herein for exemplary embodimentsof the determination of an optimal window are generally lesssophisticated than the schema used in the tissue classification module132 in FIG. 1. Alternatively, a model based on the tissue classificationschema in the module 132 in FIG. 1 can be used to determine an optimalwindow for obtaining spectral data in step 104 of FIG. 1.

[0412] In exemplary embodiments for determining an optimal windowdiscussed herein, reflectance and fluorescence intensities aredown-sampled to one value every 10 nm between 360 and 720 nm. A model istrained by adding and removing intensities in a forward manner,continuously repeating the process until the model converges such thatadditional intensities do not appreciably improve tissue classification.Testing is performed by a leave-one-spectrum-out jack-knife process.

[0413]FIG. 35 shows the difference between the mean reflectance spectrafor non-CIN 2/3 tissues and CIN 2/3 tissues at three times (prior to theapplication of acetic acid (graph 976), maximum whitening (graph 978,about 60-80 seconds post-AA), and the last time data were obtained(graph 980, about 160-180 seconds post-AA)). The time corresponding tomaximum whitening was determined from reflectance data, and occursbetween about 60 seconds and 80 seconds following application of aceticacid. In the absence of acetic acid, the reflectance spectra for CIN 2/3(curve 982 of graph 976 in FIG. 35) are on average lower than non-CIN2/3 tissue (curve 984 of graph 976 in FIG. 35). Following theapplication of acetic acid, a reversal is noted—CIN 2/3 tissues havehigher reflectance than the non-CIN 2/3 tissues. The reflectance of CIN2/3 and non-CIN 2/3 tissues increase with acetic acid, with CIN 2/3showing a larger relative percent change (compare curves 986 and 988 ofgraph 978 in FIG. 35). From about 160 s to about 180 s following aceticacid, the reflectance of CIN 2/3 tissue begins to return to thepre-acetic acid state, while the reflectance of the non-CIN 2/3 groupcontinues to increase (compare curves 990 and 992 of graph 980 in FIG.35)

[0414] Discrimination function ‘spectra’ are calculated from thereflectance spectra of CIN 2/3 and non-CIN 2/3 tissues shown in FIG. 35as one way to determine an optimal window for obtaining spectral data.Discrimination function, spectra comprise values of the discriminationfunction in Equation 76 determined as a function of wavelength for setsof spectral data obtained at various times. As shown in FIG. 36, thelargest differences (measured by the largest absolute values ofdiscrimination function) are found about 60 s to about 80 s post-aceticacid (curve 1002), and these data agree with the differences seen in themean reflectance spectra of FIG. 35 (curves 986 and 988 of graph 978 inFIG. 35).

[0415] Multivariate linear regression analysis takes into accountwavelength interdependencies in determining an optimal data acquisitionwindow. One way to do this is to classify spectral data shown in FIG. 35using a model developed from the reflectance data for each of the binsin Table 1. Then, the accuracy of the models for each bin is computedand compared. Reflectance intensities are down-sampled to one aboutevery 10 nm between about 360 nm and about 720 nm. The model is trainedby adding intensities in a forward-stepped manner. Testing is performedwith a leave-one-spectrum-out jack-knife process. The results of thelinear regression show which wavelengths best separate CIN 2/3 fromnon-CIN 2/3, as shown in Table 2. TABLE 2 Forwarded selected bestreflectance wavelengths for classifying CIN 2/3 from non-CIN 2/3 spectraobtained at different times pre and post-AA. Time from AA (s) LDA ModelInput Wavelengths (nm) Accuracy −30 370 400 420 440 530 570 590 610 6630 420 430 450 600 74 50 360 400 420 430 580 600 74 70 360 370 420 430560 580 600 77 90 360 420 430 540 590 73 110 360 440 530 540 590 71 130360 420 430 540 590 71 150 370 400 430 440 540 620 660 690 720 72 170490 530 570 630 650 75

[0416] As shown in Table 2, the two best models for separating CIN 2/3and non-CIN 2/3, taking into account wavelength interdependence, usereflectance data obtained at peak CIN 2/3 whitening (from about 60 s toabout 80 s) and reflectance data obtained from about 160 s to about 180s post acetic acid. The first model uses input wavelengths between about360 and about 600 nm, while the second model uses more red-shiftedwavelengths between about 490 and about 650 nm. This analysis shows thatthe optimal windows are about 60 s-80 s post AA and about 160-180 postAA (the latest time bin). This is consistent with the behavior of thediscrimination function spectra shown in FIG. 6.

[0417]FIG. 37 demonstrates one step in determining an optimal window forobtaining spectral data, for purposes of discriminating between CIN 2/3and non-CIN 2/3 tissue. FIG. 37 shows a graph 1006 depicting theperformance of the two LDA models described in Table 2 above as appliedto reflectance spectral data obtained at various times followingapplication of acetic acid 1008. Curve 1010 in FIG. 37 is a plot of thediagnostic accuracy of the LDA model based on reflectance spectral dataobtained between about 60 and about 80 seconds (“peak whitening model”)as applied to reflectance spectra from the bins of Table 1, and curve1012 in FIG. 37 is a plot of the diagnostic accuracy of the LDA modelbased on reflectance spectral data obtained between about 160 and about180 seconds, as applied to reflectance spectra from the bins of Table 1.For the peak-whitening model, the highest accuracy was obtained at about70 s, while accuracies greater than 70% were obtained with spectracollected in a window between about 30 s and about 130 s. The 160-180 smodel had a narrower window around 70 s, but performs better at longertimes.

[0418]FIG. 38 shows the difference between the mean 337-nm fluorescencespectra for non-CIN 2/3 tissues and CIN 2/3 tissues at three times(prior to application of acetic acid (graph 1014), maximum whitening(graph 1016, about 60 to about 80 seconds post-AA), and at a timecorresponding to the latest time period in which data was obtained(graph 1018, about 160 to about 180 seconds post-AA)). The timecorresponding to maximum whitening was determined from reflectance data,and occurs between about 60 seconds and 80 seconds following applicationof acetic acid. In the absence of acetic acid, the fluorescence spectrafor CIN 2/3 tissue (curve 1020 of graph 1014 in FIG. 38) and for non-CIN2/3 tissue (curve 1022 of graph 1014 in FIG. 38) are essentiallyequivalent with a slightly lower fluorescence noted around 390 nm forCIN 2/3 sites. Following the application of acetic acid, thefluorescence of CIN 2/3 and non-CIN 2/3 tissues decrease, with CIN 2/3showing a larger relative percent change (compare curves 1024 and 1026of graph 1016 in FIG. 38). From about 160 s to about 180 s followingacetic acid application, the fluorescence of CIN 2/3 tissue shows signsof returning to the pre-acetic acid state while the fluorescence of thenon-CIN 2/3 group continues to decrease (compare curves 1028 and 1030 ofgraph 1018 in FIG. 38).

[0419] An optimal data acquisition window may also be obtained using adiscrimination function calculated from fluorescence spectra of CIN 2/3and non-CIN 2/3 tissues shown in FIG. 38. In one example, discriminationfunction spectra include values of the discrimination function inEquation 76 determined as a function of wavelength for sets of spectraldata obtained at various times. FIG. 39 shows a graph 1032 depicting thediscrimination function spectra evaluated using the fluorescence data ofFIG. 38 obtained prior to application of acetic acid, and at two timespost-AA. As shown in FIG. 39, applications of acetic acid improves thatdistinction between CIN 2/3 and non-CIN 2/3 tissues using fluorescencedata. The largest absolute values are found using data measured withinthe range of about 160-180 s post-acetic acid (curve 1042), and theseagree with the differences seen in the mean fluorescence spectra of FIG.38 (curves 1030 and 1028 of graph 1018 in FIG. 38).

[0420] Multivariate linear regression takes into account wavelengthinterdependencies in determining an optimal data acquisition window. Anapplication of one method of determining an optimal window includesclassifying data represented in the CIN 2/3, CIN 1, and NED categoriesin the Appendix Table into CIN 2/3 and non-CIN 2/3 categories by usingclassification models developed from the fluorescence data shown in FIG.38. Fluorescence intensities are down-sampled to one about every 10 nmbetween about 360 and about 720 nm. The model is trained by addingintensities in a forward manner. Testing is performed by aleave-one-spectrum-out jack-knife process. The result of this analysisshows which wavelengths best separate CIN 2/3 from non-CIN 2/3, as shownin Table 3. TABLE 3 Forwarded selected best 337-nm fluorescencewavelengths for classifying CIN 2/3 from non-CIN 2/3 spectra obtained atdifferent times pre and post-AA. Time from AA (s) LDA Model InputWavelengths (nm) Accuracy −30 380, 430, 440, 610, 660, 700, 710 61 30370, 380, 390, 640 61 50 410 54 70 360, 390, 490, 580, 590, 670 63 90370, 380, 420, 460, 500, 560, 660 64 110 360, 390, 400, 710 51 130 37053 150 370, 380, 440, 620, 640, 700 65 170 370, 480, 510, 570, 600, 700,720 76

[0421] As shown in Table 3, the two best models for separating CIN 2/3and non-CIN 2/3, taking into account wavelength interdependencies, usedata obtained at peak CIN 2/3 whitening (60-80 s) and data obtained atthe latest time measured (from about 160 s to about 180 s post aceticacid). The first model uses input wavelengths between about 360 andabout 670 nm, while the second model uses wavelengths between about 370and about 720 nm.

[0422]FIG. 40 demonstrates one step in determining an optimal window.FIG. 40 shows a graph 1044 depicting the performance of the two LDAmodels described in Table 3 above as applied to fluorescence spectraldata obtained at various times following application of acetic acid1046. Curve 1048 in FIG. 40 is a plot of the diagnostic accuracy of theLDA model based on fluorescence spectral data obtained between about 60and about 80 seconds (“peak whitening model”) as applied to fluorescencespectra from the bins of Table 1, and curve 1050 in FIG. 40 is a plot ofthe diagnostic accuracy of the LDA model based on fluorescence spectraldata obtained between about 160 and about 180 seconds, as applied tofluorescence spectra from the bins of Table 1. The accuracies of thesemodels vary depending on when the fluorescence spectra are recordedrelative to the application of acetic acid, as shown in FIG. 40. Thepredictive ability of the fluorescence models in FIG. 40 tend to be lessthan that of the reflectance models in FIG. 37. Accuracies greater than70% are obtained with spectra collected after about 160 seconds post-AA.

[0423] One embodiment includes classifying spectral data shown in FIG.38 from known reference tissue samples into CIN 2/3 and non-CIN 2/3categories by using classification models developed from thefluorescence data for each of the bins in Table 1. Models are developedbased on time post acetic acid. Ratios of fluorescence to reflectanceare down-sampled to one every 10 nm between about 360 and about 720 nm.The model is trained by adding intensities in a forward manner. Testingis performed by a leave-one-spectrum-out jack-knife process. For thisanalysis, the model is based on intensities at about 360, 400, 420, 430,560, 610, and 630 nm. In general, the results are slightly better than amodel based on fluorescence alone. Improved performance is noted fromspectra acquired at about 160 s post acetic acid.

[0424]FIG. 41 shows a graph 1052 depicting the accuracy of three LDAmodels as applied to spectral data obtained at various times followingapplication of acetic acid 1054, used in determining an optimal windowfor obtaining spectral data. Curve 1056 in FIG. 41 is a plot of thediagnostic accuracy of the LDA model based on reflectance spectral dataobtained between about 60 and about 80 seconds (“peak whitening model”),also shown as curve 1010 in FIG. 37. Curve 1058 in FIG. 41 is a plot ofthe diagnostic accuracy of the LDA model based on fluorescence spectraldata obtained between about 60 and about 80 seconds (“peak whiteningmodel”), also shown as curve 1048 in FIG. 40. Curve 1060 in FIG. 41 is aplot of the diagnostic accuracy of the LDA model based on fluorescenceintensity divided by reflectance, as described in the immediatelypreceding paragraph.

[0425] The exemplary embodiments discussed above and illustrated inFIGS. 35 to 41 provide a basis for selecting an optimum window forobtaining spectral data upon application of acetic acid. Other factorsto be considered include the time required to apply the contrast agentand to perform target focusing as shown in FIG. 27A. Another factor isthe time required to perform a scan over a sufficient number of regionsof a tissue sample to provide an adequate indication of disease statewith sufficient sensitivity and selectivity. Also, a consideration maybe made for the likelihood of the need for and time required for retakesdue to patient motion.

[0426] The factors and analysis discussed above indicate that an optimaldata acquisition window is a period of time from about 30 secondsfollowing application of a contrast agent (for example, a 5 volumepercent acetic acid solution) to about 130 seconds following applicationof the contrast agent. Other optimal windows are possible. For example,one alternative embodiment uses an optimal window with a “start” timefrom about 10 to about 60 seconds following application of acetic acid,and an “end” time from about 110 to about 180 seconds followingapplication of acetic acid.

[0427] An alternative manner for determining an optimal window comprisesdetermining and using a relative amplitude change and/or rate ofamplitude change as a trigger for obtaining spectral data from a sample.By using statistical and/or heuristic methods such as those discussedherein, it is possible to relate more easily-monitored relative changesor rates-of-change of one or more optical signals from a tissue sampleto corresponding full spectrum signals that can be used incharacterizing the state of health of a given sample. For example, byperforming a discrimination function analysis, it may be found for agiven tissue type that when the relative change in reflectance at aparticular wavelength exceeds a threshold value, the correspondingfull-spectrum reflectance can be obtained and then used to accuratelyclassify the state of health of the tissue. In addition, the triggersdetermined above may be converted into optimal time windows forobtaining diagnostic optical data from a sample.

[0428]FIG. 42 shows how an optical amplitude trigger is used todetermine an optimal time window for obtaining diagnostic optical data.The graph 1062 in FIG. 42 plots the normalized relative change of meanreflectance signal 1064 from tissue samples with a given state of healthas a function of time following application of acetic acid 1066. Themean reflectance signal determined from CIN 1, CIN 2, and Metaplasiasamples are depicted in FIG. 42 by curves 1068, 1070, and 1072,respectively. FIG. 42 shows that when the normalized relative change ofmean reflectance reaches or exceeds 0.75 in this example, the imageintensity data and/or the full reflectance and/or fluorescence spectrumis most indicative of a given state of health of a sample. Thus, for CIN2 samples, for example, this corresponds to a time period between t₁ andt₂, as shown in the graph 1062 of FIG. 42. Therefore, spectral and/orimage data obtained from a tissue sample between t₁ and t₂ followingapplication of acetic acid are used in accurately determining whether ornot CIN 2 is indicated for that sample. In one embodiment, the relativechange of reflectance of a tissue sample at one or more givenwavelengths is monitored. When that relative change is greater than orequal to the 0.75 threshold, for example, more comprehensive spectraland/or image data are obtained to characterize whether the sample isindicative of CIN 2. In another embodiment, a predetermined range ofvalues of the relative optical signal change is used such that when therelative signal change falls within the predetermined range of values,additional spectral and/or image data is captured in order tocharacterize the sample.

[0429]FIG. 43 shows how a rate-of-change of an optical amplitude triggeris used to determine an optimal time window for obtaining diagnosticoptical data. The graph 1074 of FIG. 43 plots the slope of an exemplarymean reflectance signal 1076 from tissue samples with a given state ofhealth as a function of time following application of acetic acid 1078.The slope of mean reflectance is a measure of the rate of change of themean reflectance signal. The rate of change of mean reflectancedetermined from CIN 1, CIN 2, and metaplasia samples are depicted inFIG. 43 by curves 1080, 1082, and 1084, respectively. Those curves showthat when the absolute value of the slope is less than or equal to 0.1,for example, in the vicinity of maximum reflectance, the image intensitydata and/or the full reflectance and/or fluorescence spectrum is mostindicative of a given state of health of a sample. Thus, for CIN 2samples, for example, this corresponds to a time period between t₁ andt₂ as shown in the graph 1074 of FIG. 43. Therefore, spectral and/orimage data obtained from a tissue sample between t₁ and t₂ followingapplication of acetic acid is used in accurately determining whether ornot CIN 2 is indicated for that sample. In the example, the rate ofchange of reflectance of a tissue sample is monitored at one or morewavelengths. When that rate of change has an absolute value less than orequal to 0.1, more comprehensive spectral and/or image data are obtainedfrom the sample for purposes of characterizing whether or not the sampleis indicative of CIN 2. FIG. 43 demonstrates use of a range of values ofrate of optical signal change. Other embodiments use a single thresholdvalue.

Motion Tracking

[0430] In one embodiment, the tissue characterization system shown inFIG. 1 comprises real-time motion tracking (step 106 in FIG. 1).Real-time tracking determines a correction for and/or compensates for amisalignment between two images of the tissue sample obtained during aspectral data scan (i.e. step 732 in FIGS. 27A and 27B), where themisalignment is caused by a shift in, the position of the sample withrespect to the instrument 102 in FIG. 1 (or, more particularly, theprobe optics 178). The misalignment may be caused by unavoidable patientmotion, such as motion due to breathing during the spectral data scan732.

[0431] In one embodiment, the correction factor determined by thereal-time tracker is used to automatically compensate for patientmotion, for example, by adjusting the instrument 102 (FIG. 1) so thatspectral data obtained from indexed regions of the tissue sample duringthe scan correspond to their originally-indexed locations. Alternativelyor additionally, the motion correction factor can be used in spectraldata pre-processing, step 114 in FIG. 1 and FIG. 11, to correct spectraldata obtained during a scan according to an applicable correctionfactor. For example, the spectral data lookup method in step 114 of FIG.1 as discussed herein may compensate for patient motion by using acorrection determined by the real-time tracker 106 to correlate a set ofspectral data obtained during a scan with its true, motion-correctedposition (x,y) on the tissue sample. In one embodiment, the motioncorrection factor determined in step 106 of FIG. 1 is updated about onceevery second during the scan using successive images of the tissue, asshown in FIG. 27B. Step 106 determines and validates a motion correctionfactor about once every second during the spectral scan, correspondingto each successive image in FIG. 27B. Then, the pre-processing component114 of FIG. 1 corrects the spectral data obtained at an interrogationpoint during the spectral scan using the correction factor correspondingto the time at which the spectral data were obtained.

[0432] A typical misalignment between two images obtained about 1 secondapart is less than about 0.55-mm within a two-dimensional, 480×500 pixelimage frame field covering a tissue area of approximately 25-mm×25-mm.These dimensions provide an example of the relative scale ofmisalignment versus image size. In some instances it is only necessaryto compensate for misalignments of less than about one millimeterwithin, the exemplary image frame field defined above. In other cases,it is necessary to compensate for misalignments of less than about0.3-mm within the exemplary image frame field above. Also, thedimensions represented by the image frame field, the number of pixels ofthe image frame field, and/or the pixel resolution may differ from thevalues shown above.

[0433] A misalignment correction determination may be inaccurate, forexample, due to any one or a combination of the following:non-translational sample motion such as rotational motion, localdeformation, and/or warping; changing features of a sample such aswhitening of tissue; and image recording problems such as focusadjustment, missing images, blurred or distorted images, lowsignal-to-noise ratio, and computational artifacts. Validationprocedures of the invention identify such inaccuracies. The methods ofvalidation may be conducted “on-the-fly” in concert with the methods ofdetermining misalignment corrections in order to improve accuracy and toreduce the time required to conduct a given test.

[0434] In order to facilitate the automatic analysis in the tissueclassification system 100 of FIG. 1, it is often necessary to adjust formisalignments caused by tissue sample movement that occurs during thediagnostic procedure. For example, during a given procedure, in vivotissue may spatially shift within the image frame field from one imageto the next due to movement of the patient. Accurate tissuecharacterization requires that this movement be taken into account inthe automated analysis of the tissue sample. In one embodiment, spatialshift correction made throughout a spectral data scan is more accuratethan a correction made after the scan is complete, since “on-the-fly”corrections compensate for smaller shifts occurring over shorter periodsof time and since spectral data is being continuously obtainedthroughout the approximately 12 to 15 second scan in the embodiment ofFIG. 27B.

[0435] If a sample moves while a sequence of images is obtained, theprocedure may have to be repeated. For example, this may be because theshift between consecutive images is too large to be accuratelycompensated for, or because a region of interest moves outside of ausable portion of the frame captured by the optical signal detectiondevice. Stepwise motion correction of spectral data reduces thecumulative effect of sample movement. If correction is made only afteran entire sequence is obtained, it may not be possible to accuratelycompensate for some types of sample movement. On-the-fly, stepwisecompensation for misalignment reduces the need for retakes.

[0436] On-the-fly compensation may also obviate the need to obtain anentire sequence of images before making the decision to abort a failedprocedure, particularly when coupled with on-the-fly, stepwisevalidation of the misalignment correction determination. For example, ifthe validation procedure detects that a misalignment correctiondetermination is either too large for adequate compensation to be madeor is invalid, the procedure may be aborted before obtaining the entiresequence of images. It can be immediately determined whether or not theobtained data is useable. Retakes may be performed during the samepatient visit; no follow-up visit to repeat an erroneous test isrequired. A diagnostic test invalidated by excessive movement of thepatient may be aborted before obtaining the entire sequence of images,and a new scan may be completed, as long as there is enough remainingtime in the optimal time window for obtaining spectral data.

[0437] In preferred embodiments, a determination of misalignmentcorrection is expressed as a translational displacement in twodimensions, x and y. Here, x and y represent Cartesian coordinatesindicating displacement on the image frame field plane. In otherembodiments, corrections for misalignment are expressed in terms ofnon-Cartesian coordinate systems, such as biradical, spherical, andcylindrical coordinate systems, among others. Alternatives toCartesian-coordinate systems may be useful, for example, where the imageframe field is non-planar.

[0438] Some types of sample motion—including rotational motion, warping,and local deformation—may result in an invalid misalignment correctiondetermination, since it may be impossible to express certain instancesof these types of sample motion in terms of a translationaldisplacement, for example, in the two Cartesian coordinates x and y. Itis noted, however, that in some embodiments, rotational motion, warping,local deformation, and/or other kinds of non-translational motion areacceptably accounted for by a correction expressed in terms of atranslational displacement. The changing features of the tissue, as inacetowhitening, may also affect the determination of a misalignmentcorrection. Image recording problems such as focus adjustment, missingimages, blurred or distorted images, low signal-to-noise ratio (i.e.caused by glare), and computational artifacts may affect the correctiondetermination as well. Therefore, validation of a determined correctionis often required. In some embodiments, a validation step includesdetermining whether an individual correction for misalignment iserroneous, as well as determining whether to abort or continue the testin progress. Generally, validation comprises splitting at least aportion of each of a pair of images into smaller, corresponding units(subimages), determining for each of these smaller units a measure ofthe displacement that occurs within the unit between the two images, andcomparing the unit displacements to the overall displacement between thetwo images.

[0439] In certain embodiments, the method of validation takes intoaccount the fact that features of a tissue sample may change during thecapture of a sequence of images. For example, the optical intensity ofcertain regions of tissue change during the approximately 12 to 15seconds of a scan, due to acetowhitening of the tissue. Therefore, inone embodiment, validation of a misalignment correction determination isperformed using a pair of consecutive images. In this way, thedifference between the corresponding validation cells of the twoconsecutive images is less affected by gradual tissue whitening changes,as compared with images obtained further apart in time. In analternative embodiment, validation is performed using pairs ofnonconsecutive images taken within a relatively short period of time,compared with the time in which the overall sequence of images isobtained. In other embodiments, validation comprises the use of any twoimages in the sequence of images.

[0440] A determination of misalignment correction between two images isinadequate if significant portions of the images are featureless or havelow signal-to-noise ratio (i.e. are affected by glare). Similarly,validation using cells containing significant portions that arefeatureless or that have low signal-to-noise ratio may result in theerroneous invalidation of valid misalignment correction determinations.This may occur in cases where the featureless portion of the overallimage is small enough so that it does not adversely affect themisalignment correction determination. For example, analysis offeatureless validation cells may produce meaningless correlationcoefficients. One embodiment includes identifying one or morefeatureless cells and eliminating them from consideration in thevalidation of a misalignment correction determination, therebypreventing rejection of a good misalignment correction.

[0441] A determination of misalignment correction may be erroneous dueto a computational artifact of data filtering at the image borders. Forexample, in one exemplary embodiment, an image with large intensitydifferences between the upper and lower borders and/or the left andright borders of the image frame field undergoes Laplacian of Gaussianfrequency domain filtering. Since Laplacian of Gaussian frequency domainfiltering corresponds to cyclic convolution in the space-time domain,these intensity differences (discontinuities) yield a large gradientvalue at the image border, and cause the overall misalignment correctiondetermination to be erroneous, since changes between the two images dueto spatial shift are dwarfed by the edge effects. One alternativeembodiment employs pre-multiplication of image data by a Hamming windowto remove or reduce this “wraparound error.” However, one preferredembodiment employs an image-blending technique such as feathering, tosmooth any border discontinuity, while requiring only a minimal amountof additional processing time.

[0442]FIG. 44A represents a 480×500 pixel image 1086 from a sequence ofimages of in vivo human cervix tissue and shows a 256×256 pixel portion1088 of the image that the motion correction step 106 in FIG. 1 uses inidentifying a misalignment correction between two images from a sequenceof images of the tissue, according to one embodiment. The image 1086 ofFIG. 44A has a pixel resolution of about 0.054-mm. The embodimentsdescribed herein show images with pixel resolutions of about 0.0547-mmto about 0.0537-mm. Other embodiments have pixel resolutions outsidethis range. In some embodiments, the images of a sequence have anaverage pixel resolution of between about 0.044-mm and about 0.064-mm.In the embodiment of FIG. 44A, step 106 of the system of FIG. 1 uses thecentral 256×256 pixels 1088 of the image 1086 for motion tracking. Analternative embodiment uses a region of different size for motiontracking, which may or may not be located in the center of the imageframe field. In the embodiment of FIG. 44A, the motion tracking step 106of FIG. 1 determines an x-displacement and a y-displacementcorresponding to the translational shift (misalignment) between the256×256 central portions 1088 of two images in the sequence of imagesobtained during a patient spectral scan.

[0443] The determination of misalignment correction may be erroneous forany number of various reasons, including but not limited tonon-translational sample motion (i.e. rotational motion, localdeformation, and/or warping), changing features of a sample (i.e.whitening of tissue), and image recording problems such as focusadjustment, missing images, blurred or distorted images, lowsignal-to-noise ratio, and computational artifacts. Therefore, inpreferred embodiments, validation comprises splitting an image intosmaller units (called cells), determining displacements of these cells,and comparing the cell displacements to the overall displacement. FIG.44B depicts the image represented in FIG. 44A and shows a 128×128 pixelportion 1090 of the image, made up of 16 individual 32×32 pixelvalidation cells 1092, from which data is used to validate themisalignment correction.

[0444]FIG. 45, FIGS. 46A and B, and FIGS. 47A and B depict steps inillustrative embodiment methods of determining a misalignment correctionbetween two images of a sequence, and methods of validating thatdetermination. Steps 1096 and 1098 of FIG. 45 show development of datafrom an initial image with which data from a subsequent image arecompared in order to determine a misalignment correction between thesubsequent image and the initial image. An initial image “o” ispreprocessed, then filtered to obtain a matrix of values, for example,optical luminance (brightness, intensity), representing a portion of theinitial image. In one embodiment, preprocessing comprises transformingthe three RGB color components corresponding to a given pixel into asingle luminance value. An exemplary luminance is CCIR 601, shown inEquation 63. CCIR 601 luminance may be used, for example, as a measureof the “whiteness” of a particular pixel in an image from anacetowhitening test. Different expressions for grayscale luminance maybe used, and the choice may be geared to the specific type of diagnostictest conducted. The details of step 1096 of FIG. 45 is illustrated inblocks 1130, 1132, and 1134 of FIG. 46A, where block 1130 represents theinitial color image, “o”, in the sequence, block 1132 representsconversion of color data to grayscale using Equation 63, and block 1134represents the image of block 240 after conversion to grayscale.Referring now to FIGS. 46A and 46B, FIG. 46B is a continuation of FIG.46A, linked, for example, by the circled connectors labeled A and B.Accordingly, going forward, FIGS. 46A and 46B are referred to as FIG.46.

[0445] Step 1098 of FIG. 45 represents filtering a 256×256 portion ofthe initial image, for example, a portion analogous to the 256×256central portion 1088 of the image 1086 of FIG. 44A, using Laplacian ofGaussian filtering. Other filtering techniques are used in otherembodiments. Preferred embodiments employ Laplacian of Gaussianfiltering, which combines the Laplacian second derivative approximationwith the Gaussian smoothing filter to reduce the high frequency noisecomponents prior to differentiation. This filtering step may beperformed by discrete convolution in the space domain, or by frequencydomain filtering. The Laplacian of Gaussian (LoG) filter may beexpressed in terms of x and y coordinates (centered on zero) as shown inEquation 77: $\begin{matrix}{{{LoG}\left( {x,y} \right)} = {{- {\frac{1}{\pi \quad \sigma^{4}}\left\lbrack {1 - \frac{x^{2} + y^{2}}{2\sigma^{2}}} \right\rbrack}}^{- \frac{x^{2} + y^{2}}{2\sigma^{2}}}}} & (77)\end{matrix}$

[0446] where x and y are space coordinates and ca is the Gaussianstandard deviation. In one preferred embodiment, an approximation to theLoG function is used. Illustrative embodiments described herein includeuse of an approximation kernel(s) of size 9×9, 21×21, and/or 31×31. TheGaussian standard deviation, σ, is chosen in certain preferredembodiments using Equation 78:

σ=LoG filter size/8.49  (78)

[0447] where LoG filter size corresponds to the size of the discretekernel approximation to the LoG function (i.e. 9, 21, and 31 for theapproximation kernels used herein). Other embodiments employ differentkernel approximations and/or different values of Gaussian standarddeviation.

[0448] The LoG filter size may be chosen so that invalid scans arefailed and valid scans are passed with a minimum of error. Generally,use of a larger filter size is better at reducing large structured noiseand is more sensitive to larger image features and larger motion, whileuse of a smaller filter size is more sensitive to smaller features andsmaller motion. One embodiment of the invention comprises adjustingfilter size to coordinate with the kind of motion being tracked and thefeatures being imaged.

[0449] The details of step 1098 of FIG. 45 is illustrated in FIG. 46 inblocks 1134, 1136, and 1138 where block 1134 represents data from theinitial image in the sequence after conversion to grayscale luminance,block 1136 represents the application of the LoG filter, and block 1138represents the 256×256 matrix of data values, G_(o)(x,y), which is the“gold standard” by which other images are compared in validatingmisalignment correction determinations in this embodiment. As detailedin FIGS. 47A and 47B, one embodiment validates a misalignment correctiondetermination by comparing a given image to its preceding image in thesequence, not by comparing a given image to the initial image in thesequence as shown in FIG. 46. (Referring now to FIGS. 47A and 47B, FIG.47B is a continuation of FIG. 47A, linked, for example, by the circledconnectors labeled A, B, and C. Accordingly, going forward, FIGS. 47Aand 47B are referred to as FIG. 47.) Although FIG. 45, FIG. 46, and FIG.47 show application of the LoG filter as a discrete convolution in thespace domain, resulting in a standard expressed in space coordinates,other embodiments comprise applying the LoG filter in the frequencydomain. In either case, the LoG filter is preferably zero padded to theimage size.

[0450] The details of steps 1100 and 1102 of FIG. 45 representpreprocessing an image “i” by converting RGB values to grayscaleluminance as discussed above, and performing LoG filtering to obtainG_(i)(x,y), a matrix of values from image “i” which is compared withthat of another image in the sequence in order to determine amisalignment correction between the two images. The details of steps1100 and 1102 of FIG. 45 are illustrated in FIG. 46 in blocks 1140,1142, 1144, 1146, and 1148, where f_(i)(x,y) in block 1140 is the rawimage data from image “i”, block 1142 represents conversion of thef_(i)(x,y) data to gray scale intensities as shown in block 1144, andblock 1146 represents application of the LoG filter on the data of block1144 to produce the data of block 1148, G_(i)(x,y).

[0451] Similarly, steps 1106 and 1108 of FIG. 45 represent preprocessingan image “j” by converting RGB values to grayscale luminance asdiscussed above, and performing LoG filtering to obtain G_(j)(x,y), amatrix of values from image “j” which is compared with image “i” inorder to determine a measure of misalignment between the two images. Insome preferred embodiments, image “j” is subsequent to image “i” in thesequence. In some preferred embodiments, “i” and “j” are consecutiveimages. Steps 1106 and 1108 of FIG. 45 are illustrated in FIG. 46 inblocks 1154, 1156, 1158, 1160, and 1162, where “j” is “i+1”, the imageconsecutive to image “i” in the sequence. In FIG. 46, block 1154 is theraw “i+1” image data, block 1156 represents conversion of the “i+1” datato gray scale intensities as shown in block 1158, and block 1160represents application of the LoG filter on the data of block 1158 toproduce the data of block 1162, G_(i+1)(x,y).

[0452] Steps 1104 and 1110 of FIG. 45 represent applying a Fouriertransform, for example, a Fast Fourier Transform (FFT), using G_(i)(x,y)and G_(j)(x,y), respectively, to obtain F_(i)(u,v) and F_(j)(u,v), whichare matrices of values in the frequency domain corresponding to datafrom images “i” and “j”, respectively. Details of steps 1104 and 1110 ofFIG. 45 are illustrated in FIG. 46 by blocks 1148, 1150, 1152, 1162,1164, and 1166, where “j” is “i+1”, the image consecutive to image “i”in the sequence. In FIG. 46, block 1148 represents the LoG filtereddata, G_(i)(x,y), corresponding to image “i”, and block 1150 representstaking the Fast Fourier Transform of G_(i)(x,y) to obtain F_(i)(u,v),shown in block 1152. Similarly, in FIG. 46 block 1162 is the LoGfiltered data, G_(i+1)(x,y), corresponding to image “i+1”, and block1164 represents taking the Fast Fourier Transform of G_(i+1)(x,y) toobtain F_(i+1)(u,v), shown in block 1166.

[0453] Step 1112 of FIG. 45 represents computing the cross correlationF_(i)(u,v) F*_(j)(u,v), where F_(i)(u,v) is the Fourier transform ofdata from image “i”, F*_(j)(u,v) is the complex conjugate of the Fouriertransform of data from image “j”, and u and v are frequency domainvariables. The cross-correlation of two signals of length N₁ and N₂provides N₁+N₂−1 values; thus avoiding aliasing problems due tounder-sampling, the two signals should be padded with zeros up toN₁+N₂−1 samples. Details of step 1112 of FIG. 45 are represented in FIG.46 by blocks 1152, 1166, and 1168. Block 1168 of FIG. 46 representscomputing the cross correlation, F_(i)(u,v) F*_(i+1)(u,v), usingF_(i)(u,v), the Fourier transform of data from image “i”, andF*_(i+1)(u,v), the complex conjugate of the Fourier transform of datafrom image “i+1”. The cross-correlation may also be expressed as c(k,l)in Equation 79:

c(k,l)=ΣΣI ₁(p,q)I ₂(p−k,q−l)  (79)

[0454] where variables (k,l) can be thought of as the shifts in each ofthe x- and y-directions which are being tested in a variety ofcombinations to determine the best measure of misalignment between twoimages I₁ and I₂, and where p and q are matrix element markers.

[0455] Step 1114 of FIG. 45 represents computing the inverse Fouriertransform of the cross-correlation computed in step 1112. Step 1114 ofFIG. 45 is represented in FIG. 46 by block 1170. The resulting inverseFourier transform maps how well the 256×256 portions of images “i” and“j” match up with each other given various combinations of x- andy-shifts. Generally, the normalized correlation coefficient closest to1.0 corresponds to the x-shift and y-shift position providing the bestmatch, and is determined from the resulting inverse Fourier transform.In a preferred embodiment, correlation coefficients are normalized bydividing matrix values by a scalar computed as the product of the squareroot of the (0,0) value of the auto-correlation of each image. In thisway, variations in overall brightness between the two images have a morelimited effect on the correlation coefficient, so that the actualmovement within the image frame field between the two images is betterreflected in the misalignment determination.

[0456] Step 1116 of FIG. 45 represents determining misalignment valuesd_(x), d_(y), d, sum(d_(x)), sum(d_(y)), and Sum(d_(j)), where d_(x) isthe computed displacement between the two images “i” and “j” in thex-direction, d_(y) is the computed displacement between the two imagesin the y-direction, d is the square root of the sum d_(x) ²+d_(y) ² andrepresents an overall displacement between the two images, sum(d_(x)) isthe cumulative x-displacement between the current image “j” and thefirst image in the sequence “o”, sum(d_(y)) is the cumulativey-displacement between the current image “j” and the first image in thesequence “o”, and Sum(d_(j)) is the cumulative displacement, d, betweenthe current image “j” and the first image in the sequence “o”. Step 1116of FIG. 45 is represented in FIG. 46 by blocks 1172, 1174, and 1176.Blocks 1174 and 1176 represent finding the maximum value in the data ofblock 1172 in order to calculate d_(x), d_(y), d, sum(d_(x)),sum(d_(y)), and Sum(d_(i+1)) as described above, where image “j” in FIG.45 is “i+1” in FIG. 46, the image consecutive to image “i”. For example,in the scan illustrated by block 732 in FIG. 27B, if image “i” is theimage at block 750, then image “j” is the next consecutive image (theimage at block 752).

[0457] Steps 1118, 1120, and 1122 of FIG. 45 represent one method ofvalidating the misalignment correction determined for image “j” in step1116 of FIG. 45. This method of validating misalignment correction isrepresented in blocks 1177, 1179, 1181, 1190, 1192, and 1194 of FIG. 47.Another method of validating a misalignment correction is represented insteps 1124, 1126, and 1128 of FIG. 45; and this method is represented inblocks 1178, 1180, 1182, 1184, 1186, and 1188 of FIG. 46. FIG. 47 is aschematic flow diagram depicting steps in a version of the methods shownin FIG. 45 of determining a correction for a misalignment between twoimages in which validation is performed using data from two consecutiveimages. One embodiment includes using consecutive or near-consecutiveimages to validate a misalignment correction determination, as in FIG.47. Other embodiments comprise using the initial image to validate amisalignment correction determination for a given image, as in FIG. 46.

[0458] In FIG. 45, step 1118 represents realigning G_(j)(x,y), theLoG-filtered data from image “j”, to match up with G_(i)(x,y), theLoG-filtered data from image “i”, using the misalignment values d_(x)and d_(y) determined in step 1116. In preferred embodiments, image “j”is consecutive to image “i” in the sequence of images. Here, image “j”is image “i+1” such that G_(i)(x,y) is aligned with G_(i+1)(x,y) asshown in block 1177 of FIG. 47. Similarly, in FIG. 45, step 1124represents realigning G_(j)(x,y), the LoG-filtered data from image “j”,to match up with G_(o)(x,y), the LoG-filtered “gold standard” data fromthe initial image “o”, using the displacement values sum(d_(x)) andsum(d_(y)) determined in step 1116. Step 1124 of FIG. 45 is representedin block 1178 of FIG. 46.

[0459] Step 1120 of FIG. 45 represents comparing correspondingvalidation cells from G_(j)(x,y) and G_(i)(x,y) by computing correlationcoefficients for each cell. This is represented schematically in FIG. 47by blocks 1179, 1181, 1190, 1192, and 1194 for the case where j=i+1.First, a 128×128 pixel central portion of the realigned G_(i+1)(x,y) isselected, and the corresponding 128×128 pixel central portion ofG_(i)(x,y) is selected, as shown in blocks 1179 and 1181 of FIG. 47. Anexemplary 128×128 pixel validation region 1090 is shown in FIG. 44B.Then, one embodiment comprises computing a correlation coefficient foreach of 16 validation cells. An exemplary validation cell from each ofthe realigned G_(i+1)(x,y) matrix 1181 and G_(i)(x,y) matrix 1179 isshown in blocks 1192 and 1190 of FIG. 47. The validation cells are asdepicted in the 32×32 pixel divisions 1092 of the 128×128 pixelvalidation region 1090 of FIG. 44B. Different embodiments use differentnumbers and/or different sizes of validation cells. Correlationcoefficients are computed for each of the 16 cells, as shown in block1194 of FIG. 47. Each correlation coefficient is a normalizedcross-correlation coefficient as shown in Equation 80: $\begin{matrix}{{c^{\prime}\left( {m,n} \right)} = \frac{\sum{\sum{{I_{1}\left\lbrack {p,q} \right\rbrack} \times {I_{2}\left\lbrack {p,q} \right\rbrack}}}}{\sqrt{\sum{\sum{I_{1}^{2}\left\lbrack {p,q} \right\rbrack}}}\sqrt{\sum{\sum{I_{2}^{2}\left\lbrack {p,q} \right\rbrack}}}}} & (80)\end{matrix}$

[0460] where c′(m,n) is the normalized cross-correlation coefficient forthe validation cell (m,n), m is an integer 1 to 4 corresponding to thecolumn of the validation cell whose correlation coefficient is beingcalculated, n is an integer 1 to 4 corresponding to the row of thevalidation cell whose correlation coefficient is being calculated, p andq are matrix element markers, I₁[p,q] are elements of the cell in columnm and row n of the 128×128 portion of the realigned image shown in block1181 of FIG. 47, and I₂[p,q] are elements of the cell in column m androw n of the 128×128 portion of G_(i)(x,y) shown in block 1179 of FIG.47. In that figure, p=1 to 32 and q=1 to 32, and the sums shown inEquation 82 are performed over p and q. The cross-correlationcoefficient of Equation 80 is similar to an auto-correlation in thesense that a subsequent image is realigned with a prior image based onthe determined misalignment correction so that, ideally, the alignedimages appear to be identical. A low value of c′(m,n) indicates amismatching between two corresponding cells. The misalignment correctiondetermination is then either validated or rejected based on the valuesof the 16 correlation coefficients computed in step 1194 of FIG. 47. Forexample, each correlation coefficient may be compared against athreshold maximum value. This corresponds to step 1122 of FIG. 45.

[0461] Step 1126 of FIG. 45 represents comparing correspondingvalidation cells from G_(j)(x,y) and G_(o)(x,y) by computing correlationcoefficients for each cell. This is represented schematically in FIG. 46by blocks 1180, 1182, 1184, 1186, and 1188 for the case where j=i+1.First, a 128×128 pixel central portion of the realigned G_(i+1)(x,y) isselected, and the corresponding 128×128 pixel central portion ofG_(o)(x,y) is selected, as shown in blocks 1182 and 1180 of FIG. 46. Anexemplary 128×128 pixel validation region 1090 is shown in FIG. 44B.Then, one embodiment comprises computing a correlation coefficient foreach of the 16 validation cells. An exemplary validation cell from eachof the realigned G_(i+1)(x,y) matrix 1182 and G_(o)(x,y) matrix 1180 isshown in blocks 1186 and 1184 of FIG. 46. The validation cells are asdepicted in the 32×32 pixel divisions 1092 of the 128×128 pixelvalidation region 1090 of FIG. 44B. Other embodiments use differentnumbers of and/or different sizes of validation cells. Correlationcoefficients are computed for each of the 16 cells, as shown in block1188 of FIG. 46. Each correlation coefficient is a normalized“auto”-correlation coefficient as shown in Equation 80 above, whereI₁[p,q] are elements of the cell in column m and row n of the 128×128portion of the realigned subsequent image shown in block 1182 of FIG.46, and I₂[p,q] are elements of the cell in column m and row n of the128×128 portion of G_(o)(x,y) shown in block 1180 of FIG. 46. A lowvalue of c′(m,n) indicates a mismatching between two correspondingcells. The misalignment determination is then either validated orrejected based on the values of the 16 correlation coefficients computedin step 1188 of FIG. 46. This corresponds to step 1128 of FIG. 45.

[0462] In one embodiment, determinations of misalignment correction andvalidation of these determinations as shown in each of FIG. 45, FIG. 46,and FIG. 47 are performed using a plurality of the images in sequence.In one embodiment, determinations of misalignment correction andvalidations thereof are performed while images are being obtained, sothat an examination in which a given sequence of images is obtained maybe aborted before all the images are obtained. In some embodiments, amisalignment correction is determined, validated, and compensated for byadjusting the optical signal detection device obtaining the images. Incertain embodiments, an adjustment of the optical signal detectiondevice is made after each of a plurality of images are obtained. Incertain embodiments, an adjustment, if required by the misalignmentcorrection determination, is made after every image subsequent to thefirst image (except the last image), and prior to the next consecutiveimage. In one embodiment, a cervical tissue scan comprising a sequenceof 13 images is performed using on-the-fly misalignment correctiondetermination, validation, and camera adjustment, such that the scan iscompleted in about 12 seconds. Other embodiments comprise obtainingsequences of any number of images in more or less time than indicatedhere.

[0463] Each of steps 1122 and 1128 of the embodiment of FIG. 45represents applying a validation algorithm to determine at least thefollowing: (1) whether the misalignment correction can be made, forexample, by adjusting the optical signal detection device, and (2)whether the misalignment correction determined is valid. In an exemplaryembodiment, the validation algorithm determines that a misalignmentcorrection cannot be executed during an acetowhitening exam conducted oncervical tissue in time to provide sufficiently aligned subsequentimages, if either of conditions (a) or (b) is met, as follows: (a)d_(i), the displacement between the current image “i” and theimmediately preceding image “i−1” is greater than 0.55-mm or (b)Sum(d_(i)), the total displacement between the current image and thefirst image in the sequence, “o”, is greater than 2.5-mm. If either ofthese conditions is met, the spectral scan in progress is aborted, andanother scan must be performed. If sufficient time remains within theoptimal time window for obtaining spectral data, a fresh scan may beginimmediately after a previous scan is aborted. Other embodiments maycomprise the use of different validation rules. In one embodiment, ifonly condition (a) is met, the system retakes image “i” while continuingthe spectral scan, and if condition (b) is met, the spectral scan isaborted and must be restarted if sufficient time remains within theoptimal window.

[0464] In one embodiment, validation is performed for each determinationof misalignment correction by counting how many of the correlationcoefficients c′_(r)(m,n) shown in Equation 80 (corresponding to the 16validation cells) is less than 0.5. If this number is greater than 1,the scan in progress is aborted. In one embodiment, if there are morethan three correlation coefficients c′_(r)(m,n) less than 0.35, then thescan is aborted. Other embodiments comprise the use of differentvalidation rules. Gradual changes in image features, such asacetowhitening of tissue or changes in glare, cause discrepancies whichare reflected in the correlation coefficients of the validation cells,but which do not represent a spatial shift. Thus, in preferredembodiments, the validation is performed as shown in FIG. 47, wherevalidation cells of consecutive images are used to calculate thecorrelation coefficients. In other embodiments, the validation isperformed as shown in FIG. 46, where validation cells of a currentimage, “i”, and an initial image of the sequence, “o”, are used tocalculate the correlation coefficients of Equation 80.

[0465] FIGS. 48A-F depict a subset of adjusted, filtered images 1200,1204, 1208, 1212, 1216, and 1220 from a sequence of images of a tissuewith an overlay of gridlines showing the validation cells used invalidating the determinations of misaligment correction between theimages, according to an illustrative embodiment of the invention. Byperforming validation according to FIG. 47, using consecutive images tocalculate the correlation coefficients of Equation 80, the number ofvalidation cells with correlation coefficient below 0.5 for themisalignment-corrected images of FIGS. 48A-F is 0, 1, 0, 0, and 1 forimages 1204, 1208, 1212, 1216, and 1220, respectively. Since none of theimages have more than one coefficient below 0.5, this sequence issuccessful and is not aborted. There is only a gradually changing glare,seen to move within the validation region 1202, 1206, 1210, 1214, 1218,1222 of each image. In an embodiment in which validation is performed asin FIG. 46, the number of validation cells with correlation coefficientbelow 0.5 for the misalignment-corrected images of FIGS. 48A-F is 3, 4,5, 5, and 6 for images 1204, 1208, 1212, 1216, and 1220, respectively.This is not a good result in this example, since the exam would beerroneously aborted, due only to gradual changes in glare or whiteningof tissue, not uncompensated movement of the tissue sample.

[0466] Alternatively, validation cells that are featureless or have lowsignal-to-noise ratio are eliminated from consideration. Those cells canproduce meaningless correlation coefficients. Featureless cells in apreferred embodiment are identified and eliminated from consideration byexamining the deviation of the sum squared gradient of a givenvalidation cell from the mean of the sum squared gradient of all cellsas shown in Equation 81:

IF ssg ₁(m,n)<Mean[ssg(m,n)]−STD[ssg(m,n)], THEN set c′₁(m,n)=1.0.  (81)

[0467] where c′₁(m,n) is the correlation of the given validation cell“1”, ssg₁(m,n)=ΣΣI₁ ²[p,q], m=1 to 4, n=1 to 4, I₁[p,q] is the matrix ofvalues of the given validation cell “1”, p=1 to 32, q=1 to 32, thesummations ΣΣ are performed over pixel markers p and q, Mean[ssg(m,n)]is the mean of the sum squared gradient of all 16 validation cells, andSTD[ssg(m,n)] is the standard deviation of the sum squared gradient ofthe given validation cell “1” from the mean sum squared gradient. Bysetting c′₁(m,n)=1.0 for the given validation cell, the cell does notcount against validation of the misalignment correction determination inthe rubrics of either step 1122 or step 1128 of FIG. 45, since acorrelation coefficient of 1.0 represents a perfect match.

[0468] If an image has large intensity differences between the upper andlower borders and/or the left and right borders of the image framefield, LoG filtering may result in “wraparound error.” A preferredembodiment employs an image blending technique such as “feathering” tosmooth border discontinuities, while requiring only a minimal amount ofadditional processing time.

[0469]FIG. 49A depicts a sample image 1224 after application of a9-pixel size [9×9] Laplacian of Gaussian filter (LoG 9 filter) on anexemplary image from a sequence of images of tissue, according to anillustrative embodiment of the invention. The filtered intensity valuesare erroneous at the top edge 1226, the bottom edge 1228, the right edge1232, and the left edge 1230 of the image 1224. Since LoG frequencydomain filtering corresponds to cyclic convolution in the space-timedomain, intensity discontinuities between the top and bottom edges of animage and between the right and left edges of an image result inerroneous gradient approximations. These erroneous gradientapproximations can be seen in the dark stripe on the right edge 1232 andbottom edge 1228 of the image 1224, as well as the light stripe on thetop edge 1226 and the left edge 1230 of the image 1224. This oftenresults in a misalignment correction determination that is too small,since changes between the images due to spatial shift are dwarfed by theedge effects. A preferred embodiment uses a “feathering” technique tosmooth border discontinuities and reduce “wraparound error.”

[0470] Feathering comprises removal of border discontinuities prior toapplication of a filter. In preferred embodiments, feathering isperformed on an image before LoG filtering, for example, between steps1100 and 1102 in FIG. 45. In embodiments where LoG filtering isperformed in the frequency domain (subsequent to Fouriertransformation), feathering is preferably performed prior to bothFourier transformation and LoG filtering. For two-dimensional imageintensity (luminance) functions II(x,y) and I₂(x,y) that arediscontinuous at x=x₀, an illustrative feathering algorithm is asfollows: $\begin{matrix}{{{I_{1}^{\prime}\left( {x,y} \right)} = {{{{I_{1}\left( {x,y} \right)} \cdot {f\left( {\frac{x - x_{0}}{d} + 0.5} \right)}}\quad {and}\quad {I_{2}^{\prime}\left( {x,y} \right)}} = {{I_{2}\left( {x,y} \right)} \cdot \left( {1 - {f\left( {\frac{x - x_{0}}{d} + 0.5} \right)}} \right)}}},{{f(x)} = \left\{ {\begin{matrix}0 & {x < 0} \\{{3x^{2}} - {2x^{3}}} & {0 \leq x \leq 1} \\0 & {x > 1}\end{matrix},} \right.}} & (82)\end{matrix}$

[0471] where I₁ (x,y) and I₂′(x,y) are the intensity (luminance)functions I₁(x,y) and I₂(x,y) after applying the feathering algorithm ofEquation 82, and d is the feathering distance chosen. The featheringdistance, d, adjusts the tradeoff between removing wraparound error andsuppressing image content.

[0472]FIG. 49B depicts the application of both a feathering techniqueand a LoG filter on the same unfiltered image used in FIG. 49A. Thefeathering is performed to account for border processing effects,according to an illustrative embodiment of the invention. Here, afeathering distance, d, of 20 pixels was used. Other embodiments useother values of d. The filtered image 1234 of FIG. 49B does not displayuncharacteristically large or small gradient intensity values at the topedge 1236, bottom edge 1238, right edge 1242, or left edge 1240, sincediscontinuities are smoothed prior to LoG filtering. Also, there isminimal contrast suppression of image detail at the borders. Pixelsoutside the feathering distance, d, are not affected. The use offeathering here results in more accurate determinations of misalignmentcorrection between two images in a sequence of images.

[0473] Another method of border smoothing is multiplication ofunfiltered image data by a Hamming window. In some embodiments, aHamming window function is multiplied to image data before Fouriertransformation so that the border pixels are gradually modified toremove discontinuities. However, application of the Hamming windowsuppresses image intensity as well as gradient information near theborder of an image.

[0474]FIG. 50A is identical to FIG. 49A and depicts the application of aLoG 9 filter on an exemplary image from a sequence of images of tissueaccording to an illustrative embodiment of the invention. The filteredintensity values are erroneous at the top edge 1226, the bottom edge1228, the right edge 1232, and the left edge 1230 of the image 1224.

[0475]FIG. 50B depicts the application of both a Hamming window and aLoG 9 filter on the same unfiltered image used in FIG. 50A. Hammingwindowing is performed to account for border processing effects,according to an illustrative embodiment of the invention. Each of theedges 1246, 1248, 1250, 1252 of the image 1244 of FIG. 50B no longershow the extreme filtered intensity values seen at the edges 1226, 1228,1230, 1232 of the image 1224 of FIG. 50A. However, there is a greatersuppression of image detail in FIG. 50B than in FIG. 49B. Thus, for thisparticular embodiment, application of the feathering technique ispreferred over application of Hamming windowing.

[0476] One embodiment includes removing cyclic convolution artifacts byzero padding the image prior to frequency domain filtering to assureimage data at an edge would not affect filtering output at the oppositeedge. This technique adds computational complexity and may increaseprocessing time.

[0477] FIGS. 51A-F depict the determination of a misalignment correctionbetween two images using methods including the application of LoGfilters of various sizes, as well as the application of a Hamming windowtechnique and a feathering technique, according to illustrativeembodiments of the invention. Image 1254 and image 1256 of FIGS. 51A-Bare consecutive images from a sequence of images of cervix tissueobtained during a diagnostic exam, each with a pixel resolution of about0.054-mm. FIGS. 51C-F depict the application of four different imagefiltering algorithms: (1) Hamming window with LoG 9 filtering, (2)feathering with LoG 9 filtering, (3) feathering with LoG 21 filtering,and (4) feathering with LoG 31 filtering. Each of these algorithms areimplemented as part of a misalignment correction determination andvalidation technique as illustrated in FIG. 45 and FIG. 47, and valuesof d_(x) and d_(y) between images 1254 and 1256 of FIGS. 51A-B aredetermined using each of the four filtering algorithms. For image 1254,each of the four different image filtering algorithms (1)-(4) listedabove are applied, resulting in images 1258, 1262, 1266, and 1270,respectively, each having 256×256 pixels. The four different imagefiltering algorithms are also applied for image 1256, resulting inimages 1260, 1264, 1268, and 1272, respectively, each having 256×256pixels. Values of (d_(x), d_(y)) determined using Hamming+LoG 9filtering are (−7, 0), expressed in pixels. Values of (d_(x), d_(y))determined using feathering+LoG 9 filtering are (−2, −10). Values of(d_(x), d_(y)) determined using feathering+LoG 21 filtering are (−1,−9). Values of (d_(x), d_(y)) determined using feathering+LoG 31filtering are (0, −8). All of the displacement values determined usingfeathering are close in this embodiment, and agree well withvisually-verified displacement. However, in this example, thedisplacement values determined using Hamming windowing are differentfrom those obtained using the other three filtering methods, and resultin a misalignment correction that does not agree well withvisually-verified displacement. Thus, for this example, feathering worksbest since it does not suppress as much useful image data.

[0478] The effect of the filtering algorithm employed, as well as thechoice of validation rules are examined by applying combinations of thevarious filtering algorithms and validation rules to pairs of sequentialimages of tissue and determining the number of “true positives” and“false positives” identified. A true positive occurs when a badmisalignment correction determination is properly rejected by a givenvalidation rule. A false positive occurs when a good misalignmentcorrection determination is improperly rejected as a failure by a givenvalidation rule. The classification of a validation result as a “truepositive” or a “false positive” is made by visual inspection of the pairof sequential images. In preferred embodiments, whenever true failuresoccur, the scan should be aborted. Some examples of situations wheretrue failures occur in certain embodiments include image pairs betweenwhich there is one or more of the following: a large non-translationaldeformation such as warping or tilting; a large jump for which motiontracking cannot compute a correct translational displacement; rotationgreater than about 3 degrees; situations in which a target laser is lefton; video system failure such as blur, dark scan lines, or frameshifting; cases where the image is too dark and noisy, in shadow; caseswhere a vaginal speculum (or other obstruction) blocks about half theimage; other obstructions such as sudden bleeding.

[0479] In one embodiment, a set of validation rules is chosen such thattrue positives are maximized and false positives are minimized.Sensitivity and specificity can be adjusted by adjusting choice offiltering algorithms and/or choice of validation rules. Table 4 showsthe number of true positives (true failures) and false positives (falsefailures) determined by a validation rule as depicted in FIG. 45 andFIG. 47 where validation is determined using consecutive images. Table 4shows various combinations of filtering algorithms and validation rules.The four filtering algorithms used are (1) Hamming windowing with LoG 9filtering, (2) feathering with LoG 9 filtering, (3) feathering with LoG21 filtering, and (4) feathering with LoG 31 filtering. The values,c′(m,n), correspond to the normalized “auto”-correlation coefficient ofEquation 80 whose value must be met or exceeded in order for avalidation cell to “pass” in an embodiment. The “Number Threshold”column indicates the maximum number of “failed” validation cells, out ofthe 16 total cells, that are allowed for a misalignment correctiondetermination to be accepted in an embodiment. If more than this numberof validation cells fail, then the misalignment correction determinationis rejected. TABLE 4 True positives and false positives of validationdeterminations for embodiments using various combinations of filteringalgorithms and validation rules. Number c′ (m, n) Threshold TP FPHamming LoG 9 −0.1 1 34 28 Feathering LoG 9 −0.1 3 19 17 Feathering LoG21 0.3 2 46 10 0.35 3 52 4 Feathering LoG 31 0.5 3 48 3

[0480] For the given set of cervical image pairs on which the methodsshown in Table 4 were applied, feathering performs better than Hammingwindowing, since there are more true positives and fewer falsepositives. Among different LoG filter sizes, LoG 21 and LoG 31 performsbetter than LoG 9 for both tracking and validation here. The LoG 21filter is more sensitive to rotation and deformation than the LoG 31filter for these examples. One embodiment of the determination andvalidation of misalignment corrections between 256×256 pixel portions ofimages of cervical tissue with pixel resolution of about 0.054-mmemploys one or more of the following: (1) use of feathering for imageborder processing, (2) application of LoG 21 filter, (3) elimination ofvalidation cells with low signal-to-noise ratio, and (4) use ofconsecutive images for validation.

Broadband Reflectance Arbitration and Low-Signal Masking

[0481] A tissue characterization system as shown in FIG. 1 also maycomprise arbitrating between two or more redundant sets of spectral dataas depicted in step 128 of FIG. 1. In one embodiment shown in FIG. 1,step 128 includes arbitrating between two sets of broadband reflectancedata obtained in step 104 during a spectral scan for each interrogationpoint of a tissue sample. Data are obtained at each interrogation pointusing light incident to the interrogation point at two different angles,as depicted in FIG. 8. In this way, if only one set of reflectance datais affected by an artifact such as glare or shadow, the other set can beused in tissue classification, for example, in step 132 of FIG. 1. Thearbitration step 128 in FIG. 1 determines whether either of the two setsof reflectance spectral data at each point is affected by an artifact.Step 128 also determines a single set of reflectance data from eachinterrogation point to be used in tissue classification if at least oneof the two sets is acceptably unaffected by an artifact. As used here,artifacts identified in the arbitration step 128 of FIG. 1 include, forexample, both lighting artifacts and obstruction artifacts—such asglare, shadow, blood, mucus, a speculum, smoke tube tissue, and/or ostissue.

[0482] In the embodiment shown in FIG. 1, step 128 additionally includesa first-level “hard masking” of certain interrogation points. Forexample, interrogation points are considered “indeterminate” wherevalues of both sets of reflectance spectral data and/or values of theset of fluorescence data are low due to shadow or an obstruction.Additional spectral masks, both hard masks and soft masks, aredetermined in one embodiment in step 130 of FIG. 1. As discussed herein,hard-masking of data includes eliminating identified, potentiallynon-representative data from further consideration and identifying thecorresponding tissue region as “indeterminate”, while soft-maskingincludes applying a weighting function or weighting factor toidentified, potentially non-representative data so that the importanceof the data as a diagnostic indicator of a tissue region in a tissueclassification algorithm is thereby reduced. A point that is soft-maskedis not necessarily identified as “indeterminate”.

[0483] The diagram 284 of FIG. 8 shows that a misalignment of the probe142 may create conditions where either or both of the top and bottomspeculum blades 286 block part or all of the illumination path fromeither or both of the intersecting upper and lower cones of illuminatinglight 196,198, thereby affecting the spectral data obtained for theregion 250 of the tissue sample 194. The speculum blades, or otherobstructions present during a spectral scan, may physically obstruct theregion 250 being analyzed, or may partially obstruct the lightilluminating the region 250 causing a shadow. In either case, thespectral data obtained may be adversely affected and rendered unusablefor characterizing the region of the tissue sample. Obtaining multiplesets of spectral data using illumination from sources at variouspositions and angles improves the chances of obtaining at least one setof spectral data that is not affected by glare, shadow, and/orobstructions.

[0484]FIG. 52 shows a graph 1276 depicting exemplary mean values ofreflectance spectral data 1278 as a function of wavelength 1280 fortissue regions affected by glare 1282, tissue regions affected by shadow1284, and tissue regions affected by neither glare nor shadow 1286according to an illustrative embodiment of the invention. Thereflectance spectral data 1278 represent the fraction of incident lightthat is reflected from the sample. The graph 1276 shows that thereflectance values of a region of tissue affected by glare 1282 arehigher at all measured wavelengths than the reflectance of a region oftissue not affected by glare 1286. The graph 1276 also shows that thereflectance values of a region of tissue with illumination partiallyblocked by a speculum blade such that the region is in shadow 1284, arelower at all measured wavelengths than the reflectance of a region oftissue not affected by shadow 1286. The shapes of all three curves 1282,1284, 1286 are different. In this example, the data affected by glare orshadow may not be usable to determine a condition or characteristic ofthe region of the sample, if the data are not representative of theregion of the tissue sample. Hence, glare and shadow may adverselyaffect spectral data obtained for a region of a tissue sample.

[0485] In one embodiment, step 104 of FIG. 1 comprises obtaining onefluorescence spectrum and two broadband reflectance spectra at each of aplurality of scan locations of the sample tissue (interrogation points).Here, a spectrum refers to a collection of spectral data over a range ofwavelengths. In one embodiment method, spectral data are collected overa range of wavelengths between 360 and 720 nm in 1 nm increments. Inother embodiments, the range of wavelengths lies anywhere between about190 nm and 1100 nm. Here, the two reflectance spectra are referred to asthe BB1 (broadband one) and BB2 (broadband two) spectra. BB1 and BB2differ in the way that the tissue is illuminated at the time thespectral data are obtained as described below. In the embodiment shownin FIG. 6, the probe head 192 has 4 illumination sources 222, 224, 226,228 located circumferentially about the collection optics 200. Twosources are above 222, 224 and two are below the horizontal plane 226,228, as illustrated in the second arrangement 212 of FIG. 6. The twoupper sources are used to obtain BB1 spectra and the two lower sourcesare used to obtain BB2 spectra. Since the upper and lower sourcesilluminate a region of the tissue sample using light incident to theregion at different angles, an artifact—for example, or shadow—mayaffect one of the two reflectance spectra obtained for the region, whilethe other reflectance spectrum is unaffected. For example, duringacquisition of spectral data, the BB1 spectrum may be unaffected by anartifact even if the BB2 spectrum is adversely affected by the artifact.In such a case, BB1 spectral data may be used to characterize thecondition of the region of tissue, for example, in step 132 of FIG. 1,even though the BB2 data is not representative of the region. In otherembodiments, the BB1 and BB2 spectra comprise one or more other types ofspectral data, such as absorbance spectra, adsorption spectra,transmission spectra, fluorescence spectra, and/or other types ofoptical and atomic emission spectra.

[0486]FIG. 53 shows a graph 1287 depicting mean values and standarddeviations of broadband reflectance spectral data using the BB1 channellight source for regions confirmed as being obscured by blood, obscuredby mucus, obscured by glare from the BB1 source, obscured by glare fromthe BB2 source, or unobscured, according to an illustrative embodimentof the invention. Various sample test points corresponding to regions oftissue from patient scans were visually identified as having blood,mucus, or glare present. A sample point was identified as having bloodpresent if it was completely covered by blood and if there was no glare.A sample point was identified as having mucus present if it wascompletely covered by mucus and if there was no glare. A sample pointwas identified as having glare based on visual evidence of glare andlarge reflectance values in at least one of the two sets of reflectancespectral data (the BB1 spectrum or the BB2 spectrum). FIG. 53 shows therange of BB1 reflectance values 1288 for a given category of the sampletest points which lie within one standard deviation of the mean for thecategory, plotted as a function of wavelength 1290. FIG. 53 shows rangesof BB1 reflectance values 1288 for each of the following categories ofsample test points: those identified as having blood present 1292, thoseidentified as having mucus present 1294, those identified as havingglare from the BB1 illumination source 1296, those identified as havingglare from the BB2 illumination source 1298, and those identified asunobstructed tissue 1300.

[0487] Similarly, FIG. 54 shows a graph 1301 depicting mean values andstandard deviations of broadband reflectance spectral data using the BB2channel light source for regions confirmed as being obscured by blood1304, obscured by mucus 1306, obscured by glare from the BB1 source1308, obscured by glare from the BB2 source 1310, or unobscured 1312,according to an illustrative embodiment of the invention. FIG. 54 showsthe range of BB2 reflectance values 1302 for a given category of thesample test points which lie within one standard deviation of the meanfor the category, plotted as a function of wavelength 1290. FIG. 54shows ranges of BB2 reflectance values 1302 for each of the followingcategories of sample test points: those identified as having bloodpresent 1304, those identified as having mucus present 1306, thoseidentified as having glare from the BB1 illumination source 1308, thoseidentified as having glare from the BB2 illumination source 1310, andthose identified as unobstructed tissue 1312.

[0488]FIGS. 53 and 54 show that a region with glare from oneillumination source does not necessarily have high reflectance valuescorresponding to data obtained using the other illumination source. Forexample, in FIG. 53, the range of BB1 reflectance values 1288 of pointswith visual evidence of glare from the BB2 source 1298 is similar to therange of BB1 reflectance values 1288 of unobstructed tissue 1300.Similarly, in FIG. 54, the range of BB2 reflectance values 1302 ofpoints demonstrating glare from the BB1 source 1308 is similar to therange of BB2 reflectance values 1302 of unobstructed tissue 1312.Therefore, one of the two sets of reflectance spectral data may beuseful in characterizing the tissue even if the other of the two sets iscorrupted by an artifact, such as glare.

[0489] It may also be desirable to determine spectral characteristicscaused by various artifacts so that data corresponding to a regionaffected by a given artifact may be identified or to determine aspectral characteristic of an artifact based on the spectral dataitself, without having to rely on other visual evidence of a givenartifact. In order to determine these spectral characteristics, anembodiment of the invention comprises using spectral data known to beaffected by a given artifact based on visual evidence, as well asspectral data known not to be affected by an artifact. Techniques thatmay be used to identify spectral characteristics and/or to developclassification rules determining whether given data are affected by anartifact include, for example, discriminant analysis (linear, nonlinear,multivariate), neural networks, principal component analysis, anddecision tree analysis. One embodiment comprises determining aparticular wavelength that gives the greatest difference between theartifact-affected spectral data (the outlier) and spectral data fromcorresponding nearby tissue that is known to be unaffected by theartifact (the tissue). Alternatively, the embodiment comprisesdetermining a wavelength that gives the largest difference between theoutlier and the tissue, as weighted by a measure of variability of thedata. In one embodiment, this method locates where the differencebetween the mean reflectance for the outlier and the tissue is at amaximum relative to the difference between the standard deviations forthe outlier data and the tissue data. In one embodiment, the methoddetermines a maximum value of D as a function of wavelength, where D isthe difference given in Equation 83 below: $\begin{matrix}{{{D(\lambda)} = \frac{{{\mu \left( {{BB}(\lambda)} \right)}_{Outlier} - {\mu \left( {{BB}(\lambda)} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{BB}(\lambda)} \right)}_{Outlier} + {\sigma^{2}\left( {{BB}(\lambda)} \right)}_{Tissue}}}},} & (83)\end{matrix}$

[0490] where μ(BB(λ))_(Outlier) is the mean of a set of reflectancespectral data at wavelength λ known to be affected by a given artifact,μ(BB(λ))_(Tissue) is the mean of a set of reflectance spectral data atwavelength λ that is known not to be affected by the artifact,σ(BB(λ))_(Outlier) is the standard deviation of the set of reflectancespectral data at wavelength λ known to be affected by the givenartifact, and σ(BB(λ))_(Tissue) is the standard deviation of the set ofreflectance spectral data at wavelength λ known not to be affected bythe given artifact.

[0491]FIG. 55 shows a graph 1313 depicting the weighted difference 1314between the mean reflectance values of glare-obscured regions andunobscured regions of tissue as a function of wavelength 1316, accordingto an illustrative embodiment of the invention. The weighted difference1314 is as given in Equation 83. For the data sets used in FIG. 55, thewavelength providing the maximum value 1318 of D in Equation 83 is about420 nm. Thus, exemplary spectral characteristics identifiable with thisset of glare-obscured “outlier” data include the reflectance spectraldata at around 420 nm, and any deviation of this data from reflectancespectral “tissue” data for unobscured regions of correspondingly similartissue at around 420 nm. This embodiment uses reflectance spectral data.Other embodiments may use other types of spectral data, includingfluorescence data.

[0492]FIG. 56 shows a graph 1319 depicting the weighted difference 1314between the mean reflectance values of blood-obscured regions andunobscured regions of tissue as a function of wavelength 1316, accordingto an illustrative embodiment of the invention. The weighted differenceis as given in Equation 83. For the data sets used in FIG. 56, thewavelength providing the maximum value 1320 of D in Equation 83 is about585 nm.

[0493] Thus, exemplary spectral characteristics identifiable with thisset of blood-obscured “outlier” data include the reflectance spectraldata at about 585 nm, and any deviation of this data from reflectancespectral “tissue” data for unobscured regions of correspondingly similartissue at about 585 nm. This embodiment uses reflectance spectral data.Other embodiments may use other types of spectral data, includingfluorescence spectral data.

[0494]FIG. 57 shows a graph 1321 depicting the weighted difference 1314between the mean reflectance values of mucus-obscured regions andunobscured regions of tissue as a function of wavelength 1316, accordingto an illustrative embodiment of the invention. The weighted differenceis as given in Equation 83. For the data sets used in FIG. 57, thewavelength providing the maximum value 1322 of D in Equation 83 is about577 nm. Thus, exemplary spectral characteristics identifiable with thisset of mucus-obscured “outlier” data include the reflectance spectraldata at about 577 nm, and any deviation of this data from reflectancespectral “tissue” data for unobscured regions of correspondingly similartissue at about 577 nm. This embodiment uses reflectance spectral data.Other embodiments may use other types of spectral data, includingfluorescence spectral data.

[0495] One illustrative embodiment comprises determining two wavelengthswhere the ratio of spectral data at the two wavelengths is mostdifferent for the artifact-affected spectral data (the “outlier”) andspectral data from corresponding nearby tissue that is known to beunaffected by the artifact (the “tissue”). Alternatively, the methodcomprises determining two wavelengths where the ratio of spectral dataat the two wavelengths weighted by a measure of variability is mostdifferent for the outlier data and the tissue data. In one embodiment,the method comprises determining a maximum value of D as a function ofwavelength, where D is the difference given in Equation 84 below:$\begin{matrix}{{D = \frac{{{\mu \left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} - {\mu \left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} + {\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}},} & (84)\end{matrix}$

[0496] where μ(BB(λ)/BB(λ′))_(Outlier) is the mean of the ratios ofreflectance at wavelength λ and reflectance at wavelength λ′ for a setof reflectance spectral data known to be affected by a given artifact,μ(BB(λ)/BB(λ′))_(Tissue) is the mean of the ratios of reflectance atwavelength λ and reflectance at wavelength λ′ for a set of reflectancespectral data that is known not to be affected by the given artifact,σ(BB(λ)/BB(λ′))_(Outlier) is the standard deviation of the ratios ofreflectance at wavelength λ and reflectance at wavelength λ′ for a setof reflectance spectral data known to be affected by the given artifact,and σ(BB(λ)/BB(λ′))_(Tissue) is the standard deviation of the ratios ofreflectance at wavelength λ and reflectance at wavelength λ′ for a setof reflectance spectral data known not to be affected by the givenartifact.

[0497]FIG. 58 shows a graph 1323 depicting a ratio of the weighteddifferences 1324 between the mean reflectance values of glare-obscuredregions and unobscured regions of tissue at two wavelengths, a numeratorwavelength 1326 and a denominator wavelength 1328, according to anillustrative embodiment of the invention. The weighted difference 1324is as given in Equation 84. For the data sets used in FIG. 58, the twowavelengths providing the maximum value of D in Equation 84 are about401 nm (numerator) and about 404 nm (denominator). Thus, exemplaryspectral characteristics identifiable with this set of glare-obscured“outlier” data include the ratio of reflectance spectral data at about401 nm and the reflectance spectral data at about 404 nm, as well as anydeviation of this ratio from those of corresponding regions of similarbut unobscured tissue. This embodiment uses reflectance spectral data.Other embodiments may use other types of spectral data, includingfluorescence data.

[0498]FIG. 59 shows a graph 1325 depicting a ratio of the weighteddifferences 1324 between the mean reflectance values of blood-obscuredregions and unobscured regions of tissue at two wavelengths, a numeratorwavelength 1326 and a denominator wavelength 1328, according to anillustrative embodiment of the invention. The weighted difference is asgiven in Equation 84. For the data sets used in FIG. 59, the twowavelengths providing the maximum value of D in Equation 84 are about595 nm (numerator) and about 718 nm (denominator). Thus, an exemplaryspectral characteristic identifiable with this set of blood-obscured“outlier” data includes the ratio of the reflectance spectral data atabout 595 nm and the reflectance spectral data about 718 nm. Thisembodiment uses reflectance spectral data. Other embodiments may useother types of spectral data, including fluorescence data.

[0499]FIG. 60 shows a graph 1327 depicting a ratio of the weighteddifferences 1324 between the mean reflectance values of mucus-obscuredregions and unobscured regions of tissue at two wavelengths, a numeratorwavelength 1326 and a denominator wavelength 1328, according to anillustrative embodiment of the invention. The weighted difference is asgiven in Equation 84. For the data sets used in FIG. 60, the twowavelengths providing the maximum value of D in Equation 84 are about545 nm (numerator) and about 533 nm (denominator). Thus, an exemplaryspectral characteristic identifiable with this set of mucus-obscured“outlier” data includes the ratio of the reflectance spectral data atabout 545 nm and the reflectance spectral data at about 533 nm. Thisembodiment uses reflectance spectral data. Other embodiments may useother types of spectral data, including fluorescence data.

[0500] Another type of lighting artifact which may obscure spectral datais shadow, which may be caused, for example, by an obstruction blockingpart of the light from an illumination source on the optical probe 142of the embodiment apparatus. It may be important to differentiatebetween glare and shadow, so that spectral data representingunobstructed tissue can be properly identified. In an embodiment,broadband reflectance is expressed as the intensity of light diffuselyreflected from a region of the tissue, I_(t), over the intensity ofincident light, I_(o), at the region. When glare is measured in additionto light diffusely reflected from the tissue, a percentage of theoriginal intensity of incident light is included in the tissuereflectance measurement, so that the “reflectance” reading of a regionof a sample experiencing glare, R_(g)(λ), may be expressed as inEquation 85:

R _(g)(λ)=(I ₁(λ)+αI _(o)(λ)/I _(o)(λ)  (85)

[0501] where α is a real number between 0.0 and 1.0; I₁(λ) is theintensity of light diffusely reflected from the region of tissue atwavelength λ, and I_(o)(λ) is the intensity of light incident on theregion of the sample at wavelength λ. The intensity of thespecularly-reflected light is αI_(o)(λ). When the region of the sampleis shadowed, only a portion of the incident intensity reaches theregion. Thus, the “reflectance” reading of a region of a sampleexperiencing shadow, R_(s)(λ) may be expressed as in Equation 86:

R _(s)(λ)=βI_(t)(λ))/I _(o)(λ).  (86)

[0502] where β is a real number between 0.0 and 1.0; I_(t)(λ) is theintensity of light at wavelength λ diffusely reflected from the regionof tissue with an incident light intensity of I_(o)(λ), and I_(o)(λ) isthe intensity of light at wavelength λ that would be incident on theregion of the sample if unshadowed.

[0503] In one embodiment, the arbitration in step 128 of FIG. 1comprises determining if only one set of a pair of sets of spectral datais affected by a lighting artifact, such as glare or shadow, each sethaving been obtained using light incident on the sample at a uniqueangle. If it is determined that only one set of a pair of sets ofspectral data is affected by the artifact, then the other set ofspectral data may be used in the determination of a characteristic ofthe region of the sample, for example. In one embodiment, it isdetermined that there is evidence of a lighting artifact in the spectraldata. Such evidence may be a large difference between the reflectancemeasurements of the two sets of spectral data. If such evidence exists,then one of the reflectance measurements will either be R_(g) or R_(s),as given by Equation 85 and Equation 86. In cases where members of onlyone set are affected by a lighting artifact, the remaining set ofreflectance measurements may be expressed as R, the intensity of lightdiffusely reflected from the region of the tissue, I_(t), divided by theintensity of light incident on the region of the tissue, I_(o). In anembodiment method, the larger of the two reflectance measurementscorresponding to a given wavelength is divided by the smaller. In caseswhere only one of the sets is affected by a lighting artifact, theresulting quotient will be either R_(g)/R, which is equal to1+αI_(o)(λ)/I₁(λ), or R/R_(S), which is equal to the constant, 1/β. Ifglare is present, the value of the quotient will depend on wavelengthand the plot of the quotient as a function of wavelength should looklike an inverted unobstructed tissue broadband signal because of theαI_(o)(λ)/I_(t)(λ) term. If shadow is present, the plot of the quotientshould be constant across the spectrum.

[0504]FIG. 61 shows a graph 1332 depicting as a function of wavelength1336 mean values and confidence intervals of a ratio 1334 of BB1 and BB2broadband reflectance spectral values (larger value divided by smallervalue) for regions confirmed as being either glare-obscured orshadow-obscured tissue, according to an illustrative embodiment of theinvention. The shadow points 1338 yield a nearly constant value, whilethe glare points 1340 vary over the range of wavelength 1336 in a mannerthat resembles the inverse of unobstructed tissue reflectance. Thus,FIG. 61 illustrates an embodiment in which it is determined whether onlyone set of a pair of sets of spectral data is affected by either glareor shadow, such that the other set is unaffected by glare or shadow andmay be used to determine a characteristic of the tissue, for example. Inan embodiment, the method comprises differentiating between glare andshadow by observing the steep slope of glare-affected reflectancespectral measurements between about 577 nm and 599 nm, for example,compared to the nearly flat slope of shadow-affected reflectancespectral measurements at those wavelengths, as seen in FIG. 61.

[0505] In one embodiment, the arbitration in step 128 of FIG. 1 includesapplying and/or developing spectral artifact classification rules(metrics) using spectral data, including one or more sets offluorescence and broadband reflectance data obtained using light at oneor more angles. In one embodiment, one set of fluorescence data and twosets of reflectance data are obtained from a given region of a tissuesample (interrogation point), where each of the two sets of reflectancedata are obtained using light incident on the region at a differentangle. These metrics determine what data is representative of a givenregion of tissue. By varying the metrics, desired levels of sensitivityand selectivity of a resulting tissue characterization usingtissue-representative data may be achieved.

[0506] The following metrics are applied in one embodiment of thearbitration in step 128 of FIG. 1 and were determined using theembodiments discussed above. These metrics were developed using one setof fluorescence data and two sets of reflectance data, BB1 and BB2, forsamples of cervical tissue. Other embodiments use other combinations ofspectral data sets. Each of the two sets of reflectance data used in thefollowing metrics were obtained using light incident to a region of asample at different angles. An embodiment of the invention uses any orall of the metrics listed below to determine if any set of data shouldbe eliminated from use in determining a characteristic of a region oftissue, due to the presence of a spectral artifact. In an embodiment ofthe invention, wavelengths within a range of the wavelengths shown beloware used. In one embodiment, this range about the wavelengths is about±10 nm. In an embodiment of the invention, only certain parts of themetrics shown below are used. In one embodiment, only a portion of agiven set of spectral data are eliminated, not the entire set. In oneembodiment, BB1 and BB2 reflectance data are obtained, but fluorescencedata is not. Here, “eliminate data” means to eliminate data fromconsideration in an analysis, for example, an analysis to determine acondition of a region. It is possible to change sensitivity andselectivity of a tissue diagnostic algorithm by varying the metricsbelow, for instance by changing one or more of the threshold constants.Such variations are within an embodiment of this invention. The metricsfor one exemplary embodiment are as follows:

[0507] Glare Metric #1: Eliminate BB1 data IF:

[0508] I. {BB1(419)>0.25 AND BB1(699)>0.51} OR

[0509] BB1(529)/BB1(543)<1.0;

[0510] OR II. Max{|ΔBB|/avgBB}(370-710)>0.25 AND BB1(419)>0.18

[0511] AND BB1(699)>0.51 AND

[0512] {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}>1.1;

[0513] OR III. Max{|ΔBB|/avgBB}(370-710)>0.4 AND

[0514] {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}>1.1 AND BB2(699)>0.3.

[0515] Glare Metric #2: Eliminate BB2 data IF:

[0516] I. {BB2(419)>0.25 AND BB2(699)>0.51} OR

[0517] BB2(529)/BB2(543)<1.0;

[0518] OR II. Max{|ΔBB|/avgBB}(370-710)>0.25 AND BB2(419)>0.18

[0519] AND BB2(699)>0.51 AND

[0520] {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}>1.1;

[0521] OR III. Max{|ΔBB|/avgBB}(370-710)>0.4 AND

[0522] {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}>1.1 AND

[0523] BB1(699)>0.3.

[0524] Shadow Metric #1: Eliminate BB1 data IF:

[0525] I. BB2(499)>BB1(499) AND Max{|ΔBB|/avgBB}(370-710)>0.25 AND

[0526] BB1(499)<0.05;

[0527] OR II. Max{|ΔBB|/avgBB}(370-710)>0.5 AND

[0528] {BB1(576)/BB2(576)}/{BB1(599)/BB2(599)}<1.1 AND

[0529] BB2(576)>BB1(576) AND BB1(419)<0.2.

[0530] Shadow Metric #2: Eliminate BB2 data IF:

[0531] I. BB1(499)>BB2(499) AND Max{|ΔBB|/avgBB}(370-710)>0.25 AND

[0532] BB2(499)<0.05;

[0533] OR II. Max{|ΔBB|/avgBB}(370-710)>0.5 AND

[0534] {BB2(576)/BB1(576)}/{BB2(599)/BB1(599)}<11.1 AND

[0535] BB1(576)>BB2(576) AND BB2(419)<0.2.

[0536] Low Signal: Eliminate BB1, BB2, and Fl data IF:

[0537] I. Fl(479)<3.5 counts/μJ (where mean fluorescent intensity ofnormal squamous tissue is about 70 counts/μJ at about 450 nm);

[0538] OR II. BB1(499)<0.035 & BB2(499)<0.035.

[0539] where BB1(X) is the BB1 reflectance spectrum measurement atwavelength λ, BB2(X) is the BB2 reflectance spectrum measurement atwavelength λ, Max{|ΔBB1|/avgBB}(370-710) indicates the maximum of {theabsolute value of the difference between the BB1 and BB2 reflectancespectrum measurements divided by the average of the BB1 and BB2measurements at a given wavelength} over the range of about 370 to 710nm, and Fl(X) is the fluorescence spectrum measurement at wavelength λ.The following are notes regarding the Metrics listed above and apply toa preferred embodiment, subject to the variations described above:

[0540] Glare Metric #1 and Glare Metric #2:

[0541] Level I: Broadband measurements are generally greater than about0.25 at about 419 nm and greater than about 0.51 at about 699 nm onlywhen there is glare in the channel (i.e. BB1 or BB2). The lack of adownward slope between about 499 and about 543 nm is also a strongindication that the broadband measurements are affected by glare.

[0542] Level II: Large percentage differences in the broadbandmeasurements combined with higher than average reflectance at about 419nm and about 699 nm also indicates the presence of glare. The presenceof a slope when the broadband measurements at about 576 nm and about 599nm are divided is further confirmation that glare is present.

[0543] Level III: A maximum broadband percent difference that is largerthan about 0.4 indicates that there is a lighting artifact present. Thepresence of a slope when the broadband measurements at about 576 andabout 599 nm are divided and an off-channel broadband greater than about0.3 at about 699 nm reveals that the lighting artifact is due to glareinstead of shadow.

[0544] If a point is identified as glare in one channel, thensubsequently identified as glare in both channels, both broadbandmeasurements should be eliminated.

[0545] Shadow Metric #1 and Shadow Metric #2:

[0546] Level I: Broadband measurements that are shadowed generally willhave a large percent difference between BB1 and BB2 and a lowreflectance at about 499 nm.

[0547] Level II: A maximum broadband percent difference that is largerthan about 0.5 indicates that there is a lighting artifact present.Lacking a large slope when the broadband measurements at about 576 andabout 599 nm are divided and an off-channel broadband less than about0.2 at about 419 nm reveals that the point is shadow instead of glare.

[0548] Cases where both BB and Fl measurements should be eliminated:

[0549] Low Signal:

[0550] Broadband measurements lower than about 0.035 at about 449 nm orfluorescence measurements lower than about 3.5 at about 479 nm indicatethat the measurements are not coming from tissue, but rather from blood,the os, smoke tube, speculum, or another obstruction. Sites withsignificant shadowing in both broadband channels are also identifiedwith this metric. Because of the uncertainty of the tissue beingmeasured, the reflectance and fluorescence data from that point areassumed invalid, regardless of whether it was identified by fluorescenceor the broadband channels.

[0551] The low signal metric acts as a hard mask because it eliminates aqualifying interrogation point from consideration by the classifier orthe other masks, such as the spectral masks in step 130 of FIG. 1. Thelow signal metric acts as a hard mask, for example, for points that haveshadowing in both BB1 and BB2.

[0552] The metrics used in this embodiment of step 128 of FIG. 1 includea low signal metric, which detects spectral data affected by obstructionartifacts such as blood, a speculum, a smoke tube, or other obstruction.This metric also identifies regions where both sets of broadbandreflectance data are affected by shadow. These were combined into onelow signal metric in this embodiment, since regions affected by theseartifacts exhibit similar characteristics, such as low fluorescence andlow broadband reflectance measurements.

[0553]FIG. 62 shows a graph 1342 depicting broadband reflectance 1344 asa function of wavelength 1346 for the BB1 channel 1348 and the BB2channel 1350 measurements for a region of tissue where the BB1 data isaffected by glare but the BB2 data is not, according to an illustrativeembodiment of the invention. The glare leads to a higher value ofreflectance 1344 than that of surrounding unaffected tissue. By applyingthe metrics listed above in step 128 of FIG. 1, it is determined thatthe exemplary BB1 set of spectral data shown in FIG. 62 is affected byglare and is thus not suitably representative of this region of thetissue sample. Applying the metrics of step 128 also determines that theBB2 set of spectral data is potentially representative of this region ofthe sample (unaffected by an artifact), since it is not eliminated. Oneembodiment comprises using this representative data in step 132 of FIG.1 to determine a condition of this region of the sample, for example,the state of health.

[0554]FIG. 63 shows a graph 1351 depicting broadband reflectance 1344 asa function of wavelength 1346 for the BB1 channel 1352 and the BB2channel 1354 broadband reflectance spectral data for a region of tissuewhere the BB2 data is affected by shadow but the BB1 data is not,according to an illustrative embodiment of the invention. The shadowleads to a lower value of reflectance 1344 than that of surroundingunaffected tissue. By applying the metrics listed above in step 128 ofFIG. 1, it is determined that the exemplary BB2 set of spectral datashown in FIG. 63 is affected by shadow and is therefore not suitablyrepresentative of this region of the tissue sample. Applying the metricsof step 128 also leads to the determination that the BB1 set of spectraldata is potentially representative of this region of the sample, sincethe BB1 set of data is not eliminated. One embodiment comprises usingthis representative data in step 132 of FIG. 1 to determine a conditionof this region of the sample, for example, the state of health.

[0555]FIG. 64 shows a graph 1358 depicting broadband reflectance 1360 asa function of wavelength 1362 for the BB1 channel 1364 and the BB2channel 1366 measurements for a region of tissue that is obscured byblood, according to an illustrative embodiment of the invention. Byapplying the metrics listed above, it is determined that blood ispresent, and that both the BB1 and the BB2 sets of spectral data areconsidered unrepresentative of this region of the tissue sample.

[0556]FIG. 65 shows a graph 1367 depicting broadband reflectance 1360 asa function of wavelength 1362 for the BB1 channel 1368 and the BB2channel 1370 measurements for a region of tissue that is unobscured,according to an illustrative embodiment of the invention. Applying thismethod determines that neither set of spectral data is affected by anartifact, and, therefore, either is representative of the tissue sample.One embodiment comprises using an average value 1372 of the BB1 and BB2measurements at each wavelength to represent the region of the tissuesample in determining a condition of this region, for example, the stateof health of the region, in step 132 of FIG. 1.

[0557] Application of the metrics listed above was performed usingvarious tissue types to verify the sensitivity and specificity of themetrics. While, in one embodiment, it is undesirable to eliminate goodspectral data of normal tissue, it is worse to eliminate good spectraldata of diseased tissue, particularly if it is desired to use the datain the classification of the state of health of a region of tissue. Thefollowing tissue types were used in the verification: tt-132 (metaplasiaby impression), tt-155 (normal by impression), tt-117 (blood), NEDpath(no evidence of disease confirmed by pathology), and cin23all (CIN 2/3diseased tissue). Table 5 shows the number of points (regions)corresponding to each of these tissue types, the determinations from themetrics listed above for these points, and the number of points whereone set of broadband reflectance spectral data were eliminated, whereboth sets of broadband reflectance spectral data were eliminated, andwhere both reflectance and fluorescence spectral data were eliminated.TABLE 5 Verification of Metrics Tissue Type cin23all nedpath tt-117tt-132a tt-155 Total pts. 477 919 175 5000 2016 Low Signal 2 14 126 2 0Glare in BB1 7 30 4 122 26 Glare in BB2 9 40 9 134 16 Glare in both 3 51 15 5 Shadow in 47 35 4 165 132 BB1 Shadow in 16 37 24 359 32 BB2 OneBB 16.6 15.5 23.4 15.6 10.2 Removed(%) Both BB 1.05% 2.07% 72.57% 0.34%0.25% Removed(%) FI Removed 0.42 1.52 72.00 0.04 0.00 (%)

[0558] For the regions (points) corresponding to CIN 2/3 diseasedtissue, no broadband reflectance measurements were unnecessarilyeliminated from the set using the above metrics. The points identifiedas being low signal were all located on the os. All points that wereidentified by the metric as shadow were verified as being correct, andonly one point identified as glare was incorrect.

[0559] For the nedpath points (no evidence of disease), only two tissuepoints were unnecessarily eliminated after being misidentified as mucus.A point that was actually dark red tissue with glare was incorrectlyidentified as shadow in BB2. The points that were identified as glarewere verified as being correct.

[0560] Out of the 175 blood points, 126 were identified as being lowsignal. The glare points and shadow points were accurate.

[0561] Out of the 5000 points in the metaplasia by impression group,there were no valid tissue points lost. The data set was improved byeliminating about 800 readings of points affected by either glare orshadow.

[0562] Out of the 2016 normal by impression points, no measurements wereunnecessarily removed from the set.

[0563]FIG. 66 shows a graph 1374 depicting the reduction in thevariability of broadband reflectance measurements 1376 of CIN2/3-confirmed tissue produced by filtering (eliminatingnon-representative spectral data) using the metrics of step 128 in FIG.1 described above, according to an illustrative embodiment of theinvention. The graph 1374 depicts mean values and standard deviations ofbroadband reflectance spectral data before and after filtering.

[0564]FIG. 67 shows a graph 1378 depicting the reduction in thevariability of broadband reflectance measurements 1376 of tissueclassified as “no evidence of disease confirmed by pathology” producedby filtering using the metrics described above, according to anillustrative embodiment of the invention. The graph 1378 depicts meanvalues and standard deviations of broadband reflectance spectral databefore and after filtering.

[0565]FIG. 68 shows a graph 1380 depicting the reduction in thevariability of broadband reflectance measurements 1376 of tissueclassified as “metaplasia by impression” produced by filtering using themetrics described above, according to an illustrative embodiment of theinvention. The graph 1380 depicts mean values and standard deviations ofbroadband reflectance spectral data before and after filtering.

[0566]FIG. 69 shows a graph 1382 depicting the reduction in thevariability of broadband reflectance measurements 1376 of tissueclassified as “normal by impression” produced by filtering using themetrics described above, according to an illustrative embodiment of theinvention. The graph 1382 depicts mean values and standard deviations ofbroadband reflectance spectral data before and after filtering.

[0567]FIG. 70A depicts an exemplary image of cervical tissue 1388divided into regions for which two types of reflectance spectral dataand one type of fluorescence spectral data are obtained, according toone embodiment of the invention. FIG. 70B is a representation 1398 ofthe regions depicted in FIG. 70A and shows the categorization of eachregion using the metrics in step 128 of FIG. 1. The black-highlightedsections 1390 of the image 1388 in FIG. 70A correspond to points(regions) that had both reflectance measurements eliminated byapplication of the embodiment method. Many of the lower points 1392, asseen in both FIGS. 70A and 70B, are in shadow because the speculumobstructs the view of one of the channels. Glare is correctly identifiedprominently at the upper one o'clock position 1394. Since there areblood points on the shadowed section, some are labeled blood (lowsignal) and others are treated as shadow.

[0568]FIG. 71A depicts an exemplary image of cervical tissue 1402divided into regions for which two types of reflectance spectral dataand one type of fluorescence spectral data are obtained, according toone embodiment of the invention. FIG. 71B is a representation 1406 ofthe regions depicted in FIG. 71A and shows the categorization of eachregion using the metrics in step 128 of FIG. 1. FIGS. 71A and 71B showan example of a cervix that has a large portion of the lower half 1404affected by shadow. However, only one of the sets of reflectancespectral data (BB2) is affected by the shadow artifact. The BB1reflectance spectral data is not affected by shadow. Applying themetrics above, the BB1 data are used to describe these regions, whilethe BB2 data are eliminated from consideration. The accuracy of tissuecharacterization using the reflectance measurements should be improvedsignificantly for this patient using the arbitration metrics of step 128of FIG. 1, since the more accurate broadband measurements will be usedin later characterization steps instead of simply averaging the twobroadband measurements, which would skew the measurements due to alighting artifact.

[0569]FIG. 72A depicts an exemplary image of cervical tissue 1410divided into regions for which two types of reflectance spectral dataand one type of fluorescence spectral data are obtained, according to anillustrative embodiment of the invention. FIG. 72B is a representation1416 of the regions depicted in FIG. 72A and shows the categorization ofeach region using the metrics in step 128 of FIG. 1. FIGS. 72A and 72Bshow an image with a portion 1412 that is shadowed and off of thecervix. Due to an obstruction from the smoke tube in the upper part ofthe image, there are many low signals. Even though much of the cervix isshadowed in BB1 1414, there are still some BB2 and fluorescence readingsusable in later tissue classification steps.

Classification System Overview

[0570] The tissue characterization system 100 of FIG. 1 combinesspectral data and image data obtained by the instrument 102 tocharacterize states of health of regions of a tissue sample. In oneembodiment, the spectral data are first motion-tracked 106, preprocessed114, and arbitrated 128 before being combined with image data in step132 of FIG. 1. Likewise, in one embodiment, the image data are firstfocused 122 and calibrated 124 before being combined with spectral datain step 132 of FIG. 1. Each of these steps are discussed in more detailherein.

[0571]FIG. 73 shows how spectral data and image data are combined in thetissue characterization system of FIG. 1, according to one embodiment.The block diagram 1420 of FIG. 73 depicts steps in processing andcombining motion-tracked 106, preprocessed 114, and arbitrated 128spectral data with focused 122, calibrated 124 image data to determinestates of health of regions of a tissue sample. After preprocessing 114,spectral data from each of the interrogation points (regions) of thetissue sample are arbitrated in step 128 of FIG. 73. In the embodimentshown, a fluorescence spectrum, F, and two broadband reflectancespectra, BB1 and BB2, are used to determine one representativereflectance spectrum, BB, used along with the fluorescence spectrum, F,for each interrogation point. This is depicted in FIG. 73 as three heavyarrows representing the three spectra—BB1, BB2, and F—enteringarbitration block 128 and emerging as two spectra—BB and F. Block 128 ofFIG. 73 also applies an initial low-signal mask as a first pass atidentifying obscured interrogation points, discussed previously herein.

[0572] In the embodiment of FIG. 73, the arbitrated broadbandreflectance spectrum, BB, is used in the statistical classificationalgorithm 134, while both the broadband reflectance spectrum, BB, andthe fluorescence spectrum, F, as well as the image data, are used todetermine heuristic-based and/or statistics-based metrics, or “masks”,for classifying the state of health of tissue at interrogation points.Masking can be a means of identifying data that are potentiallynon-representative of the tissue sample. Potentially non-representativedata includes data that may be affected by an artifact or obstructionsuch as blood, mucus, fluid, glare, or a speculum. Such data is eitherhard-masked or soft-masked. Hard-masking of data includes identifyinginterrogation points at which the data is not representative ofunobscured, classifiable tissue. This results in a characterization of“Indeterminate” at such an interrogation point, and no furthercomputations are necessary for that point. Soft-masking includesapplying a weighting function or weighting factor to identified,potentially non-representative data. The weighting is taken into accountduring calculation of disease probability and may or may not result inan indeterminate diagnosis at the corresponding tissue region.Soft-masking provides a means of weighting spectral and/or image dataaccording to the likelihood that the data is representative of clear,unobstructed tissue in a region of interest. In the embodiment shown inFIG. 73, both hard masks and soft masks are determined using acombination of spectral data and image data. Furthermore, the masks ofFIG. 73 use spectral and image data to identify interrogation pointsthat are not particularly of interest in the exam, such as the vaginalwall, smoke tube tissue, the os, or tissue outside the region ofinterest.

[0573] In addition to determining data that are potentiallynon-representative of regions of interest, the masks shown in FIG. 73also include masks that determine where the data is highly indicative ofnecrotic tissue or disease-free (NED) tissue. It has been discoveredthat necrotic tissue and disease-free tissue are often more predictablydetermined by using a heuristic metric instead of or in combination witha statistical classifier than by using a statistical classifier alone.For example, one embodiment uses certain values from fluorescencespectra to determine necrotic regions, since fluorescence spectra canindicate the FAD/NADH component and porphyrin component of necrotictissue. Also, an embodiment uses prominent features of fluorescencespectra indicative of normal squamous tissues to classify tissue as“NED” (no evidence of disease) in the spectral mask.

[0574] Identifying necrotic and NED regions at least partially by usingheuristic metrics allows for the development of statistical classifiers134 that concentrate on differentiating tissue less conducive toheuristic classification—for example, statistical classifiers thatdifferentiate high grade cervical intraepithelial neoplasia (i.e. CIN2/3) from low grade neoplasia (i.e. CIN 1) and healthy tissue.

[0575] In FIG. 73, step 130 uses the arbitrated spectra, BB and F, todetermine four spectral masks—NED_(spec) (no evidence of disease),Necrosis_(spec), [CE]_(spec) (cervical edge/vaginal wall), and[MU]_(spec) (mucus/fluid). The focused, calibrated video data is used todetermine nine image masks—Glare_(vid), Mucus_(vid), Blood_(vid),OS_(vid), [ROI]_(vid) (region of interest), [ST]_(vid) (smoke tube),[SP]_(vid) (speculum), [VW]_(vid) (vaginal wall), and [FL]_(vid) (fluidand foam). Step 1422 of FIG. 73 combines these masks to produce a hard“indeterminate” mask, a soft “indeterminate” mask, a mask identifyingnecrotic regions, and a mask identifying healthy (NED) regions. In theembodiment of FIG. 73, steps 1424 and 1426 apply the necrotic mask andhard “indeterminate” mask, respectively, prior to using the broadbandspectral data in the statistical classifiers 134, while steps 1428 and1430 apply the soft “indeterminate” mask and the NED mask after thestatistical classification step 134.

[0576] The embodiment shown in FIG. 73 can classify each interrogationpoint in step 1432 as necrotic, CIN 2/3, NED, or Indeterminate. Theremay be some post-classification processing in step 1434, for example,for interrogation points having a valid fluorescence signal but havingboth broadband signals, BB1 and BB2, eliminated by application of thearbitration metrics in step 128. The embodiment in FIG. 73 then uses thefinal result to create a disease display overlay of a reference image ofthe tissue sample in step 138. Each of the masking and classificationsteps summarized above are discussed in more detail herein.

[0577] In one alternative embodiment, the statistical classifiers instep 134 of FIG. 73 additionally include the use of fluorescence, image,and/or kinetic data. One alternative embodiment includes using differentsets of spectral and/or image masks than those in FIG. 73. Also, onealternative embodiment includes using a different order of applicationof heuristic masks in relation to one or more statistical classifiers.In one alternative embodiment, kinetic data is determined by obtainingintensity data from a plurality of images captured during a tissue scan,determining a relationship between corresponding areas of the images toreflect how they change with time, and segmenting the images based onthe relationship. For example, an average kinetic whitening curve may bederived for tissue areas exhibiting similar whitening behavior.Whitening kinetics representative of a given area may be compared toreference whitening kinetics indicative of known states of health,thereby indicating a state of health of the given area. In onealternative embodiment, the kinetic image-based data may be combinedwith spectral data to determine states of health of regions of a tissuesample.

[0578]FIG. 74 shows a block diagram 1438 depicting steps in the methodof FIG. 73 in further detail. The steps of FIG. 74 are summarized belowand are discussed in detail elsewhere herein. Steps 1440, 1442, 1444,and 1446 in FIG. 74 depict determination of the spectral masks from thearbitrated broadband reflectance and fluorescence signals, as seen instep 130 of FIG. 73. Steps 1448, 1450, 1452, 1454, 1456, 1458, 1460,1462, and 1464 in FIG. 74 depict determination of the image masks fromthe focused, calibrated video data, as seen in step 108 of FIG. 73. Thelines extending below these mask determination steps in FIG. 74 show how(in one embodiment) the masks are combined together, as indicated instep 1422 of FIG. 73. Steps 1466, 1468, 1470, 1472, 1474, 1476, 1478,and 1480 of FIG. 74 shows which masks are combined. Also important isthe manner in which the masks are combined, disclosed in the detailedstep explanations herein.

[0579] The statistical classification step 134 from FIG. 73 is shown inFIG. 74 as steps 1482, 1484, and 1486. Here, the pictured embodimentapplies a necrosis mask 1424 and a hard “indeterminate” mask 1426 to thearbitrated broadband spectral data to eliminate the need to furtherprocess certain necrotic and indeterminate interrogation points in theclassification step. Classification includes processing of broadbandspectral data via wavelength region truncation, wavelength subsampling,and/or mean-centering. The processed data is then used in two differentfeature extraction methods. These include a principal component analysis(PCA) method used in the DASCO classifier step 1484 (DiscriminantAnalysis with Shrunken Covariances) and a feature coordinate extraction(FCE) method used in the DAFE classifier step 1482 (DiscriminantAnalysis Feature Extraction). Each of steps 1484 and 1482 extract alower dimensional set of features from the spectral data that is thenused in a Bayes' classifier to determine probabilities of classificationin one or more tissue-class/state-of-health categories. Theclassification probabilities determined in steps 1482 and 1484 arecombined in step 1486. Each of the classifiers in steps 1482 and 1484are specified by a set of parameters that have been determined bytraining on known reference data. One embodiment includes updating theclassifier parameters as additional reference data becomes available.

Spectral Masking

[0580] The invention comprises determining spectral masks. Spectralmasks identify data from a patient scan that are potentiallynon-representative of regions of interest of the tissue sample. Spectralmasks also identify data that are highly indicative of necrotic tissueor normal squamous (NED) tissue. In one embodiment, the spectral masksare combined as indicated in the block flow diagram 1438 of FIG. 74, inorder to account for the identification of spectrally-maskedinterrogation points in the tissue-class/state-of-health classificationstep 1432. Steps 1440, 1442, 1444, and 1446 in FIG. 74 depict thedetermination of spectral masks from the arbitrated broadbandreflectance and fluorescence spectra obtained during a patient scan andare discussed in more detail below.

[0581] Step 1440 in FIG. 74 depicts the determination of an NED_(spec)(no evidence of disease) spectral mask using data from the fluorescencespectrum, F, and the broadband reflectance spectrum, BB, at each: of theinterrogation points of the scan pattern, following the arbitration andlow-signal masking step 128. Applying the NED_(spec) mask reduces falsepositive diagnoses of CIN 2/3 resulting from thetissue-class/state-of-health classification step 134 in FIG. 1 (and FIG.89). The NED_(spec) mask identifies tissue having optical propertiesdistinctly different from those of CIN 2/3 tissue. More specifically, inone embodiment, the NED_(spec) mask uses differences between thefluorescence signals seen in normal squamous tissue and CIN 2/3 tissue.These differences are not accounted for by tissue-class/state-of-healthclassifiers based on broadband reflectance data alone. For example, theNED_(spec) mask uses the collagen peak seen in the fluorescence spectraof normal squamous tissue at about 410 nm to distinguish normal squamoustissue from CIN 2/3 tissue.

[0582]FIG. 75 shows a scatter plot 1500 depicting discrimination betweenregions of normal squamous tissue and CIN 2/3 tissue for a set of knownreference data, according to one embodiment. Plotting fluorescenceintensity at 460 nm (y-axis, 1502) against a ratio of fluorescenceintensity, F(505 nm)/F(410 nm), (x-axis, 1504) provides gooddiscrimination between regions known to be normal squamous tissue (bluepoints in FIG. 75) and regions known to be CIN 2/3 tissue (red points inFIG. 75). One component of the NED_(spec) discrimination metric is shownby line 1506 in FIG. 75, which divides a region of the plot that ispredominately representative of normal squamous tissue (1508) from aregion of the plot that is predominately representative of CIN 2/3tissue (1510). The divider 1506 can be adjusted, for example, to furtherreduce false positives or to allow detection of more true positives atthe expense of increased false positives.

[0583] In one embodiment, the fluorescence over reflectance ratio atabout 430 nm is also included in the NED_(spec) metric to determinenormal columnar tissue sites that may not be identified by the componentof the metric illustrated in FIG. 75 (i.e. blue points on the right ofline 1506). It is found that fluorescence of CIN 2/3 tissue at about 430nm is lower relative to normal tissue, while CIN 2/3 reflectance atabout 430 nm is higher relative to normal tissue, after application of acontrast agent such as acetic acid.

[0584]FIG. 76 shows a graph 1512 depicting as a function of wavelength1514 the mean broadband reflectance values 1516 for a set of knownnormal squamous tissue regions 1518 and a set of known CIN 2/3 tissueregions 1520, used in one embodiment to determine an additionalcomponent of the NED_(spec) spectral mask. FIG. 77 shows a graph 1522depicting as a function of wavelength 1524 the mean fluorescenceintensity values 1526 for the set of known squamous tissue regions 1528and the set of known CIN 2/3 tissue regions 1530. The difference betweencurves 1528 and 1530 in FIG. 77 is pronounced. Thus, a term is includedin the NED_(spec) metric based on the best ratio of wavelengths found tomaximize values of D in the discrimination equation, Equation 87, below:$\begin{matrix}{D = \frac{{{\mu \left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Outlier} - {\mu \left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Outlier} + {\sigma^{2}\left( {{F(\lambda)}/{F\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}} & (87)\end{matrix}$

[0585] where μ indicates mean and o indicates standard deviation. FIG.78 shows a graph 1532 depicting values of D in Equation 87 using a rangeof numerator wavelengths 1536 and denominator wavelengths 1538.According to the graph 1532 in FIG. 78, values of D are maximized usingthe fluorescence ratio F(450 nm)/F(566 nm). Alternately, othercombinations of numerator wavelength and denominator wavelength may bechosen.

[0586] A scatter plot depicting discrimination between regions of normalsquamous tissue and CIN 2/3 tissue for a set of known reference data areproduced by comparing the ratio F(450 nm)/F(566 mn) to a thresholdconstant. Then, a graph of true positive ratio (TPR) versus falsepositive ratio (FPR) in the discrimination between regions of normalsquamous tissue and CIN 2/3 tissue are obtained using a thresholdconstant. For example, a TPR of 65% and an FPR of 0.9% is obtained usinga threshold constant of 4.51. The ratio of false positives may bereduced by adjusting the threshold.

[0587] Therefore, in one embodiment, the NED_(spec) mask combines thefollowing three metrics:

F(430)/BB(430)>x ₁  (88)

F(450)/F(566)>x ₂  (89)

F(460)>x ₃ ·F(505)/F(410)−x ₄  (90)

[0588] where x₁, x₂, x₃, and x₄ are constants chosen based on thedesired aggressiveness of the metric. Equations 88-90 account for thedistinguishing features of spectra obtained from regions of normalsquamous tissue versus spectra from CIN 2/3 tissue regions, as discussedabove.

[0589] FIGS. 79A-D illustrate adjustment of the components of theNED_(spec) mask metric shown in Equations 88, 89, and 90. FIG. 79Adepicts a reference image of cervical tissue 1554 from a patient scan inwhich spectral data is used in arbitration step 128, in NED_(spec)spectral masking, and in statistical classification of interrogationpoints of the tissue sample. FIG. 79B is a representation (obgram) 1556of the interrogation points (regions) of the tissue sample depicted inthe reference image 1554 of FIG. 79A and shows points that are “masked”following application of Equation 90. The obgram 1556 of FIG. 79B showsthat some additional interrogation points are masked as NED tissue byadjusting values of x₃ and x₄ in Equation 90 from {x₃=120, x₄=42} to{x₃=115, x₄=40}. FIG. 79C shows interrogation points that are “masked”following application of Equation 89. The obgram 1570 of FIG. 79C showsthat a few additional points are masked as NED tissue by adjusting thevalue of x₂ from 4.0 to 4.1. FIG. 79D shows interrogation points thatare masked following application of Equation 88. The obgram 1584 of FIG.79D shows that a few additional points are masked as NED tissue byadjusting the value of x₁ from 610 to 600.

[0590] In one embodiment values of x₁, x₂, x₃, and x₄ in Equations 88,89, and 90 are determined using multidimensional unconstrained nonlinearminimization. In one embodiment, the overall NED_(spec) metric thatresults is as follows:

F(430)/BB(430)>600 ct/μJ OR

F(450)/F(566)>4.1 OR

F(460)>115·F(505)/F(410)−40

[0591] where the mean fluorescent intensity of normal squamous tissue isabout 70 counts/μJ at about 450 nm.

[0592] Step 1442 in FIG. 74 depicts the determination ofNecrosis_(spec), a necrotic tissue spectral mask, using data from thefluorescence spectrum, F, at each of the interrogation points of thescan pattern, following the arbitration and low-signal masking step 128.Unlike the other spectral masks (steps 1440, 1442, and 1446 in FIG. 74),which are designed to reduce false positive diagnoses of CIN 2/3, theNecrosis_(spec) mask identifies areas of necrotic tissue, therebyidentifying patients with fairly advanced stages of invasive carcinoma.

[0593] In one embodiment, the Necrosis_(spec) mask uses prominentfeatures of the fluorescence spectra from a set of known necroticregions to identify necrotic tissue. For example, in one embodiment, theNecrosis_(spec) mask uses the large porphyrin peaks of necrotic tissueat about 635 nm and/or at about 695 nm in identifying necrotic tissue.FIG. 80 shows a graph 1598 depicting fluorescence intensity 1600 as afunction of wavelength 1602 from an interrogation point confirmed asinvasive carcinoma by pathology and necrotic tissue by impression, whileFIG. 81 shows a graph 1612 depicting broadband reflectance spectra BB1and BB2 for the same point.

[0594] The graph 1598 of FIG. 80 shows the distinctive porphyrin peaksat reference numbers 1604 and 1606. Concurrent with high porphyrinfluorescence at necrotic regions is a smaller peak at about 510 nm(label 1608), possibly due to flavin adenine dinucleotide (FAD), with anintensity greater than or equal to that of nicotinamide adeninedinucleotide (NADH) at about 450 nm (label 1610). The FAD/NADH ratio isa measure of ischemia and/or hypoxia indicative of advanced stages ofcancer.

[0595] Thus, in one embodiment, the overall Necrosis_(spec) metric hasone or more components indicative of FAD/NADH and one or more componentsindicative of porphyrin. In one embodiment, the Necrosis_(spec) metricis as follows:

F(510 nm)/F(450 nm)>1.0 AND

F(635 nm)/F(605 nm)>1.3 AND

F(635 nm)/F(660 nm)>1.3 AND

F(635 nm)>20 ct/μJ

[0596] where mean fluorescent intensity of normal squamous tissue isabout 70 counts/μJ at about 450 nm, and where the first line of themetric indicates FAD/NADH (FAD) and the remainder of the metricindicates porphyrin. This metric requires all components to be satisfiedin order for a region of tissue to be classified as necrotic. In oneembodiment, the combination is needed to reduce false necrosis diagnosesin patients. The presence of porphyrin does not always indicatenecrosis, and necrosis masking based solely on the detection ofporphyrin may produce an unacceptable number of false positives. Forexample, porphyrin may be present due to hemoglobin breakdown productsfollowing menses or due to systemic porphyrin resulting frommedications, bacterial infection, or porphyria. Thus, the presence ofboth porphyrin and the indication of FAD must both be determined inorder for a region to be identified as necrotic by the Necrosis_(spec)metric in the embodiment described above.

[0597]FIG. 82A depicts a reference image 1618 of cervical tissue fromthe scan of a patient confirmed as having advanced invasive cancer, inwhich spectral data is used in arbitration step 128, in Necrosis_(spec)spectral masking, and in statistical classification 134 of interrogationpoints of the tissue sample. FIG. 82B is an obgram 1620 of theinterrogation points (regions) of the tissue sample depicted in FIG. 82Aand shows points that are identified by application of the FAD componentof the Necrosis_(spec) metric above (1628), as well as points that areidentified by application of the porphyrin component of theNecrosis_(spec) metric above (1626). The overall Necrosis_(spec) maskabove identifies points as necrotic only when both FAD and porphyrin areidentified. In FIG. 82B, interrogation points that are marked by both ablue dot (FAD 1626) and a green ring (porphyrin 1626) are identified asnecrotic tissue by application of the Necrosis_(spec) metric above.

[0598] Step 1444 in FIG. 74 depicts the determination of a cervicaledge/vaginal wall spectral mask ([CE]_(spec)) using data from thefluorescence spectrum, F, and the broadband reflectance spectrum, BB, ofeach interrogation point of a scan, following the arbitration andlow-signal masking step 128. The [CE]_(spec) mask identifies low-signaloutliers corresponding to the cervical edge, os, and vaginal wall,which, in one embodiment, are regions outside an area of diagnosticinterest for purposes of the tissue characterization system 100 of FIG.1.

[0599]FIGS. 83, 84, 85, and 86 compare broadband reflectance andfluorescence spectra of cervical edge and vaginal wall regions tospectra of CIN 2/3 tissue. In one embodiment, these comparisons are usedin a discrimination analysis to determine a [CE]_(spec) spectral mask.FIG. 83 shows a graph 1638 depicting as a function of wavelength 1640the mean broadband reflectance values 1642 for a set of known cervicaledge regions 1644 and a set of known CIN 2/3 tissue regions 1646. FIG.84 shows a graph 1648 depicting as a function of wavelength 1650 themean fluorescence intensity values 1652 for the set of known cervicaledge regions 1654 and the set of known CIN 2/3 tissue regions 1656. FIG.85 shows a graph 1658 depicting as a function of wavelength 1660 themean broadband reflectance values 1662 for a set of known vaginal wallregions 1664 and a set of known CIN 2/3 tissue regions 1666. FIG. 86shows a graph 1668 depicting as a function of wavelength 1670 the meanfluorescence intensity values 1672 for the set of known vaginal wallregions 1674 and the set of known CIN 2/3 tissue regions 1676.

[0600] In one embodiment, features of the curves in FIGS. 83, 84, 85,and 86 are used in determining the [CE]_(spec) spectral mask metric. Forexample, from FIGS. 84 and 86, it is seen that reflectance values forcervical edge/vaginal wall regions are lower than CIN 2/3 reflectance,particularly at about 450 nm and at about 700 nm. From FIGS. 84 and 86,it is seen that there is a “hump” in the fluorescence curves forcervical edge regions 1654 and vaginal wall regions 1674 at about 400nm, where there is no such hump in the CIN 2/3 curve (1656/1676). Thiscauses the ratio of fluorescence intensity, F(530 nm)/F(410 nm), to below at cervical edge/vaginal wall regions, relative to that of CIN 2/3regions. From FIG. 86, the mean fluorescence intensity of vaginal wallregions 1674 is lower than that of CIN 2/3 regions at least from about500 nm to about 540 nm. In one embodiment, these observations arecombined to determine the overall [CE]_(spec) mask metric as follows:

BB(450 nm)·BB(700 nm)/BB(540 nm)<0.30 OR

F ²(530 nm)/F(410 nm)<4.75.

[0601] The top line of the metric above reflects the observation thatthe mean reflectance of cervical edge/vaginal wall tissue is comparableto that of CIN 2/3 tissue at about 540 nm and lower than that of CIN 2/3tissue at about 450 nm and about 700 nm. The bottom line of the metricabove reflects the observation that the fluorescence of a cervicaledge/vaginal wall region may have a lower fluorescence at 530 nm thanCIN 2/3 tissue and that the cervical edge/vaginal wall region may have alower F(530 nm)/F(410 nm) ratio than CIN 2/3 tissue.

[0602]FIG. 87A depicts a reference image 1678 of cervical tissue from apatient scan in which spectral data is used in arbitration and[CE]_(spec) spectral masking. FIG. 87B is an obgram 1680 of theinterrogation points (regions) of the tissue sample depicted in FIG. 87Aand shows, in yellow (1684), the points that are “masked” by applicationof the [CE]_(spec) metric above. White points (1682) in FIG. 87Bindicate regions that are filtered out by the arbitration and low-signalmask of step 128, while pink points (1686) indicate regions remainingafter application of both the arbitration/low-signal mask of step 128 aswell as the [CE]_(spec) spectral mask.

[0603] Step 1446 in FIG. 74 depicts the determination of a fluids/mucus([MU]_(spec)) spectral mask using data from the broadband reflectancespectrum, BB, at each interrogation point of the tissue sample followingthe arbitration and low-signal masking step 128. In one alternateembodiment, the fluorescence spectrum is used in place of or in additionto the broadband reflectance spectrum. The [MU]_(spec) mask identifiestissue sites covered with thick, opaque, and light-colored mucus, aswell as fluid that is pooling in the os or on top of the speculum duringa patient scan.

[0604]FIGS. 88, 89, 90, and 91 show steps in an exemplary discriminationanalysis to determine a [MU]_(spec) spectral mask. FIG. 106 shows agraph 1688 depicting as a function of wavelength 1690 the mean broadbandreflectance values 1692 for a set of known pooling fluids regions 1694and a set of known CIN 2/3 tissue regions 1696. FIG. 89 shows a graph1697 depicting as a function of wavelength 1698 the mean fluorescenceintensity values 1700 for the set of known pooling fluids regions 1702and the set of known CIN 2/3 tissue regions 1704. The difference betweencurves 1694 and 1696 in FIG. 88 is pronounced. Thus, in one embodiment,a term is included in the [MU]_(spec) mask metric based on the bestratio of wavelength found to maximize values of D in the discriminationequation, Equation 91, as follows: $\begin{matrix}{D = \frac{{{\mu \left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} - {\mu \left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}{\sqrt{{\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Outlier} + {\sigma^{2}\left( {{{BB}(\lambda)}/{{BB}\left( \lambda^{\prime} \right)}} \right)}_{Tissue}}}} & (91)\end{matrix}$

[0605] In one embodiment, values of D above are maximized using thebroadband reflectance ratio BB(594 nm)/BB(610 nm).

[0606] A scatter plot depicting discrimination between pooling fluidsregions and CIN 2/3 tissue regions for a set of known reference data areobtained by comparing the ratio of arbitrated broadband intensity,BB(594 nm)/BB(610 nm) to a threshold constant. Then, a graph of truepositive ratio (TPR) versus false positive ratio (FPR) in thediscrimination between pooling fluids regions and CIN 2/3 tissue regionsare obtained using a threshold constant. For example, a TPR of 56.3% andan FPR of 0.9% is obtained using a threshold constant of 0.74. The ratioof false positives may be reduced by adjusting the threshold.

[0607]FIG. 90 shows a graph 1722 depicting as a function of wavelength1724 the mean broadband reflectance values 1726 for a set of known mucusregions 1728 and a set of known CIN 2/3 tissue regions 1730. FIG. 91shows a graph 1732 depicting as a function of wavelength 1734 the meanfluorescence intensity values 1736 for the set of known mucus regions1738 and the set of known CIN 2/3 tissue regions 1740. The differencebetween curves 1728 and 1730 in FIG. 90 is pronounced. Thus, in oneembodiment, a term is included in the [MU]_(spec) metric based on thebest ratio of wavelength found to maximize values of D in thediscrimination equation, Equation 91 above. In one embodiment, thisratio is BB(456 nm)/BB(542 nm).

[0608] A scatter plot depicting discrimination between mucus regions andCIN 2/3 tissue regions for a set of known reference data may be obtainedby comparing the ratio of arbitrated broadband intensity, BB(456nm)/BB(542 nm) to a threshold constant. Then, a graph of true positiveratio (TPR) 1752 versus false positive ratio (FPR) 1754 in thediscrimination between mucus regions and CIN 2/3 tissue regions areobtained using a threshold constant. For example, a TPR of 30.4% and anFPR of 0.8% is obtained using a threshold constant of 1.06. The ratio offalse positives may be reduced by adjusting the threshold.

[0609] In one embodiment, the discrimination analysis illustrated inFIGS. 88, 89, 90, and 91 lead to the overall [MU]_(spec) mask metric asfollows:

BB(456 nm)/BB(542 nm)<1.06 OR

BB(594 nm)/BB(610 nm)>0.74.

[0610] The metric above combines the sites identified by the pooledfluids mask, as indicated by the bottom line of the metric above, withthe sites identified by the mucus mask, as indicated by the top line ofthe metric above.

[0611]FIG. 92A depicts a reference image 1758 of cervical tissue from apatient scan in which spectral data is used in arbitration and[MU]_(spec) spectral masking. FIG. 92B is an obgram 1770 of theinterrogation points (regions) of the tissue sample depicted in FIG. 92Aand shows, in yellow (1768), the points that are “masked” by applicationof the [MU]_(spec) metric above. White points (1766) in FIG. 92Bindicate regions that are filtered out by the arbitration and initiallow-signal mask of step 128, while pink points (1770) indicate regionsremaining after application of both the arbitration/low-signal mask ofstep 128 as well as the [MU]_(spec) spectral mask.

Image Masking

[0612] The invention also comprises an image masking feature. Imagemasks identify data from one or more images obtained during patientexamination that are potentially non-representative of regions ofinterest of the tissue sample. Potentially non-representative dataincludes data that are affected by the presence of an obstruction, suchas blood, mucus, a speculum, pooled fluid, or foam, for example. In oneembodiment, a reference image of an in-situ cervical tissue sample isobtained just prior to a spectral scan, and image masks are determinedfrom the reference image to reveal where there may be an obstruction orother area that is not of diagnostic interest. Areas that are not ofdiagnostic interest include regions affected by glare, regions of theos, vaginal wall tissue, or regions that are otherwise outside the areaof interest of the tissue sample. These areas may then be “masked” fromthe analysis of spectral data obtained from tissue regions that coincidewith the obstruction, for example. The image masks are combined witheach other and/or with the spectral masks, as shown in block 1422 ofFIG. 73 and as shown in FIG. 74. The resultant masks include “hard”masks and “soft” masks, described in more detail herein. Hard masksresult in a characterization (or diagnosis) of “Indeterminate” ataffected regions, while soft masking provides a means of weightingspectral data according to the likelihood that the data isrepresentative of clear, unobstructed tissue in a region of interest.

[0613] In one embodiment, image masks are combined and applied asindicated in the block diagram 1438 of FIG. 74, in order to account forthe identification of image-masked interrogation points in thetissue-class/state-of-health classification step 1432. Steps 1448, 1450,1452, 1454, 1456, 1458, 1460, 1462, and 1464 in FIG. 74 depict thedetermination of image masks from the image data obtained around thetime of the patient spectral scan. These image masks are discussed inmore detail below.

[0614]FIG. 93 depicts image masks 1782, 1784, 1786 determined from areference image of a tissue sample and conceptually shows how the imagemasks are combined with respect to each interrogation point (region)1790 of the tissue sample, according to one embodiment. Generally, for agiven interrogation point 1790 in the scan pattern 1788, the systemdetermines whether any of the features detected by the image masks, suchas the os image mask 1784 and the blood image mask 1786, intersects thatinterrogation point (region) 1790. For certain image masks, a percentcoverage is determined for regions they intersect. For some image masks,if any of the mask intersects a region, the region is flagged as“masked”.

[0615] In one embodiment, a backend process determines the coverage ofone or more masks for each interrogation point of the scanning pattern.Given a known correspondence between image pixels and interrogationpoints, a given point is assigned a percentage coverage value for afeature determined by a given image mask, such as blood detected by theBlood_(vid) image mask 1458 in FIG. 74. The percentage coverage valuecorresponds to the number of pixels for the given interrogation pointcoinciding with the selected image mask feature, divided by the totalnumber of pixels for that interrogation point. For example, if the bloodmask for a given interrogation point coincides with 12 out of 283 pixelsthat cover the point, then the percentage coverage for thatinterrogation point is 12/283, or 4.2%.

[0616] Steps 1468, 1470, 1472, and 1474 in FIG. 74 demonstrate how theimage masks are combined in one embodiment, and steps 1466, 1476, 1424,1478, 1480, 1424, 1426, 1428, and 1430 in FIG. 74 demonstrate how thecombined masks are applied with respect to thetissue-class/state-of-health classifications at the spectralinterrogation points, in one embodiment. These steps are discussed inmore detail herein.

[0617] The image masks in FIG. 74 are determined using image processingmethods. These methods include color representation, spatial filtering,image thresholding, morphological processing, histogram processing, andcomponent labeling methods, for example.

[0618] In one embodiment, images are obtained in 24-bit RGB format.There are a number of ways to quantify image intensity and other imagecharacteristics at each pixel. Most of the image masks in FIG. 74 usevalues of luminance (grayscale intensity) at each pixel. In oneembodiment, luminance, Y, at a given pixel is defined as follows:

Y=0.299R+0.587G+0.114B  (92)

[0619] where Y is expressed in terms of red (R), green (G), and blue (B)intensities; and where R, G, and B range from 0 to 255 for a 24-bit RGBimage. Some of the image masks in FIG. 74 use one or more of thefollowing quantities: $\begin{matrix}{{redness} = {\frac{R - G}{R + G} + \frac{R - B}{R + B}}} & (93) \\{{greenness} = {\frac{G - R}{G + R} + \frac{G - B}{G + B}}} & (94) \\{{blueness} = {\frac{B - R}{B + R} + \frac{B - G}{B + G}}} & (95)\end{matrix}$

[0620] where R, G, and B are as defined above.

[0621] Determination of the image masks in FIG. 74 includes the use ofone-dimensional (1-D) and two-dimensional (2-D) filters. The types offilters used includes low-pass, smoothing filters and gradient, edgedetection filters. The 1-D filters generally range in size from 3 to 21pixels and the 2-D filters generally range from 3×3 to 15×35 pixels,although other filter sizes may be used. In one embodiment, box carfilters are the preferred type of low-pass (smoothing) filters. Box carfilters replace the value at the center of the filter support with anequally-weighted average of all pixels within the filter support. In oneembodiment, the preferred types of gradient filters are Sobel andLaplacian of Gaussian filters.

[0622] In one embodiment, the image masks in FIG. 74 are determinedusing image thresholding, a subclass of image segmentation in which theimage is divided into two segments. The criterion for assigning a pixelto one of the two segments is whether its value is less than, largerthan, or equal to a prescribed threshold value. A binary image may beobtained by marking pixels having values less than the threshold withzeros and the remaining pixels with ones. Some image masks aredetermined using multiple thresholding and/or dynamic thresholding,where the threshold for each pixel or group of pixels is computeddynamically from image statistics, for example.

[0623] In one embodiment, the determination of the image masks in FIG.74 includes binary morphological processing. Binary morphologicalprocessing is performed on a binarized (thresholded) image to smoothobject boundaries, change the size of objects, fill holes withinobjects, remove small objects, and/or separate nearby objects.Morphological operators used herein include dilation, erosion, opening,and closing. An operator may be defined by (1) a binary mask orstructuring element, (2) the mask origin, and (3) a mathematicaloperation that defines the value of the origin of the mask. In oneembodiment, a 3×3 square structuring element is used, and is generallypreferred unless otherwise specified.

[0624] In one embodiment, dilation increases the size of a binary objectby half the size of the operator mask/structuring element. Erosion isthe inverse of dilation and decreases the size of a binary object. Forexample, an erosion of a binary object is equivalent to the dilation ofthe background (non-objects). Opening is an erosion followed by adilation, and closing is a dilation followed by an erosion. As usedherein, dil(Img, n) denotes performing n dilation steps on image 1 mgwith a 3×3 square structuring element, and erod(Img, n) denotesperforming n erosion steps on image 1 mg with a 3×3 square structuringelement.

[0625] In one embodiment, the determination of the image masks in FIG.74 includes the use of histograms. Here, a histogram relates intervalsof pixel luminance values (or other quantification) to the number ofpixels that fall within those intervals. In one embodiment, histogramprocessing includes smoothing a histogram using a 1-D low-pass filter,detecting one or more peaks and/or valleys (maxima and minima), and/orcomputing thresholds based on the peaks and/or valleys.

[0626] In one embodiment, the determination of the image masks in FIG.74 includes component labeling. Component labeling is used to joinneighboring pixels into connected regions that comprise the components(objects) in an image. Extracting and labeling of various disjoint andconnected components (objects) in an image allows separate analysis foreach object.

[0627] In component labeling of a binary image using 8-connectivity, aconnected components labeling operator scans the image by moving alongthe row until coming to a pixel p with a value V=1, then the operatorexamines the four neighbors of p that have already been encountered inthe scan. For example, the four neighbors of p are (1) the pixel to theleft of p, (2) the pixel directly above p, and (3,4) the two pixels inthe row above pixel p that are diagonal to pixel p. Based on thisinformation, p is labeled as follows:

[0628] If all four neighbors have V=0, assign a new label to p, ELSE

[0629] If only one neighbor has V=0, assign its label to p, ELSE

[0630] If one or more neighbors have a value of 1, assign one of thelabels to p and note the equivalences.

[0631] After completing the scan, the equivalent label pairs are sortedinto equivalence classes and a unique label is assigned to each class. Asecond scan is made through the image, and each label is replaced by thelabel assigned to its equivalence class. Component labeling of a binaryimage with 4-connectivity may be performed similarly.

[0632] In one embodiment, an image mask is determined using data from arepresentative image of a tissue sample obtained near to the time of aspectral scan of the tissue (just before, during, and/or just after thespectral scan). In one embodiment, the representative image is obtainedwithin about 30 seconds of the beginning or ending of the spectral scan;in another embodiment, the representative image is obtained within about1 minute of the beginning or ending of the spectral scan; and in anotherembodiment, the representative image is obtained within about 2 minutesof the beginning or ending of the spectral scan. Other ranges of time inrelation to the spectral scan are possible. In one embodiment, there isonly one reference image from which all the image masks are determined.

Glare_(vid)

[0633] Step 1462 in FIG. 74 depicts the determination of a glare mask,Glare_(vid), for an image of a tissue sample. Glare_(vid) indicatesregions of glare in a tissue image. Glare_(vid) is also used in thecomputation of other image masks. FIG. 94A depicts an exemplary image1794 of cervical tissue used to determine a corresponding glare imagemask, Glare_(vid). FIG. 94B represents a binary glare image mask,Glare_(vid), 1796 corresponding to the tissue image 1794 in FIG. 94A.

[0634] The white specks of glare in the tissue image 1794 in FIG. 94Aare identified by the image mask 1796. The image mask is determinedusing an adaptive thresholding image processing procedure. Differentthresholds are applied in different areas of the image, since the amountof illumination may vary over the image, and a threshold luminanceindicative of glare in one area of the image may not indicate glare inanother, lighter area of the image. In one embodiment, for example, animage of a tissue sample is divided into a 4 by 4 grid of equally-sized,non-overlapping blocks. A suitable glare threshold is computed for eachblock, and the subimage within that block is binarized with the computedthreshold to yield a portion of the output glare segmentation mask,Glare_(vid). Each block computation is independent, and blocks areserially processed until the complete binary glare mask, Glare_(vid), iscompletely calculated. For each block, multiple thresholds based onluminance value and/or histogram shape are computed and are used todetect and process bimodal distributions.

[0635]FIG. 95 is a block diagram depicting steps in a method ofdetermining a glare image mask, Glare_(vid), for an image of cervicaltissue. Step 1802 in FIG. 95 indicates dividing an image into a 4×4 gridof cells (blocks) 1804 and computing a histogram for each cell that isthen used to determine thresholds 1806 applicable to that block. Eachhistogram correlates intervals of luminance values, Y, (Y ranging from 0to 255) to the number of pixels in the cell (subimage) having luminancevalues within those intervals.

[0636] Step 1806 in FIG. 95 indicates determining thresholds applicableto a given cell of the image. For example, FIG. 96 shows a histogram1842 for one cell of an exemplary image. Curve 1848 indicates a rawhistogram plot for the cell (subimage), and curve 1850 indicates thecurve after 1-D filtering using a 21-point box car filter. Quantities1840 related to thresholding that are calculated from each histogram1842 include T_(pk) (peak), T_(vy) (valley), T_(lp), T_(s), T_(do), andT₉₀, all of which are described below. The exemplary histogram 1842 inFIG. 96 shows bars indicating values of T_(pk) (1852), T_(vy) (1854),T_(lp) (1856), T_(s) (1858), T_(do) (1860), and T₉₀ (1862) for the cellhistogram curve. The heavy dashed line (1854) indicates the finalthreshold chosen for the cell according to the method of FIG. 95.

[0637] The following describes the steps of the method 1800 shown inFIG. 95, according to one embodiment.

[0638] The method 1800 in FIG. 95 comprises calculating intendedthresholds in step 1806. Four thresholds are computed to decide whetherthe block (cell) contains glare:

[0639] 1. Ts=mean+3*std where mean is the average intensity of the blockand std its standard deviation.

[0640] 2. Tlp=last peak of smoothed histogram. Smoothing is performedusing a width 5 maximum order statistic filter.

[0641] 3. Tdo=Lmax+2 (Ldo−Lmax) where Lmax is the index (gray level) atwhich the 21-point boxcar filtered histogram, sHist, reaches it maximumvalue sHistMax, and Ldo is the first point after Lmax at which thefiltered histogram value falls below 0.1*sHistMax.

[0642] 4. T90 is defined so that 90% of the graylevels greater than 210are greater than T90.

[0643] Next, the method 1800 in FIG. 95 includes a block (cell) glaredetector in step 1810. The block (cell) glare detector assesses whetherglare is present in the block and selects the next block if no glare isdetected. The block is assumed to have no glare if the followingcondition is met:

((Tlp<Ts) AND (Ts<T90)) OR

((Tlp<Tdo) AND (Tdo<T90)) OR

((Tlp<Tdo) AND (Tlp<Ts) AND (Tlp<T90)) OR

((Tlp<0.8*T90) AND (no valid glare mode as described in the bimodalhistogram detection section below)).

[0644] Next, the method 1800 in FIG. 95 comprises selecting a candidatethreshold, Tc, in step 1812. A candidate threshold Tc is chosen basedupon the values of the intermediate thresholds Ts, Tlp, Tdo and T90according to the following rules: 1. if (Tlp < T90): a. if (Tdo < Tlp/2): i. if (Ts < Tlp): Tc = (Ts + Tlp) / 2 ii. else Tc = Tlp b. else Tc= min (Tdo, Tlp) 2. (Tlp >= T90) High intensity glare a. if (Ts <= T90):i. if ((Ts <= 100) AND (Tdo <= 100)): Tc = max (Ts, Tdo) ii. else if((Ts <=100) and (Tdo > 100): Tc = min (Tdo, Tlp) iii. else Tc = min (Ts,Tdo) b. else i. if (Tdo < 100): Tc = T90 ii. else Tc = min (Tdo, T90).

[0645] Next, the method 1800 in FIG. 95 includes detecting a bimodalhistogram in step 1806. Step 1806 detects bimodal histograms that arelikely to segment glare from non-glare and uses the 21 point boxcarfiltered histogram sHist to determine Tvy after computing Tpk andTcross, as described herein. To compute Tpk, sHist is searched backwardsfrom the end until point Tpk where the value is greater than the meanand maximum of its 5 closest right and left neigbors and where Tpk isgreater or equal to 10. Tcross is the point after Tpk (in backwardssearch) where the histogram value crosses over the value it has at Tpk.If the histogram is unimodal, Tpk is equal to Lmax, the graylevel whereshist attains its max value, and Tcross is 0. Tvy is the minimum pointon sHist between Tpk and Tcross if the following glare condition, calledvalid glare mode, is met:

(Tpk>175) AND (Tpk>Lmax) AND

(sHist[tPk]<0.6*sHist[Lmax]) AND

((Tpk−Tcross>20) OR (Tpk>T90)) AND

((Tpk>(mean+(1.5*std))) OR (Tpk>T90)).

[0646] Next, the method 1800 in FIG. 95 includes selecting a finalthreshold in steps 1814, 1816, 1818, 1820, 1822, 1824, and 1826. Thefinal threshold selected depends on whether the histogram is bimodal orunimodal. For a bimodal histogram with a valid glare mode, the finalthreshold T is Tvy if 175<Tvy<Tc. In all other cases (i.e. for unimodalhistograms with a candidate threshold Tc and for bimodal histograms witha valley threshold Tvy ouside the range 175 to Tc), Tc is chosen as thefinal threshold unless it can be incremented untilsHist[Tc]<0.01*Shist[Lmax] or Tc>Tlim under the following twoconditions. First, if a value L exists in the range [Tc,255] wheresHist[L]>sHist[Tc], define Lmin to be the gray value where sHist reachesits minimum in the range [Tc,L]. Then, Tc should not be incrementedbeyond Lmin, and the limit threshold TLim=Lmin. If L<150, then Tlim=210.Secondly, if L does not exist, Tlim=210.

[ROI]_(vid)

[0647] Step 1448 in FIG. 74 depicts the determination of a generalregion-of-interest mask, [ROI]_(vid), for an image of a tissue sample.The general region-of-interest mask determines where there is tissue inan image, and removes the non-tissue background. [ROI]_(vid) is alsoused in the computation of other image masks. FIG. 97A depicts anexemplary image 1894 of cervical tissue used to determine acorresponding region-of-interest mask, [ROI]_(vid), 1896 correspondingto the tissue image 1894 in FIG. 97A. The mask 1896 excludes thenon-tissue pixels in image 1894.

[0648] The [ROI]_(vid) mask detects the general areas of the imageindicative of tissue, and is determined by thresholding a pre-processedred channel image of the tissue and by performing additional processingsteps to remove unwanted minor regions from the thresholded image,explained in more detail below.

[0649]FIG. 98 is a block diagram 1900 depicting steps in a method ofdetermining a region-of-interest image mask, [ROI]_(vid), for an imageof cervical tissue. The following describes the steps of the methodshown in FIG. 98 (1900), according to one embodiment.

[0650] The method 1900 includes pre-processing in step 1902. First,smooth the red channel image by twice applying a 5×5 box car filter. Thefiltered image is sRed. Next, compute a best dynamic threshold for sRedas follows. Create a foreground binary image of sRed using a thresholdof 15. Create a glare mask binary image, glareMsk, using glare maskprocess Glare_(vid) above. Create a valid cervix pixel image, validPix,by binary AND-ing foreground and glareMsk inverse. Binary erodevalidPix, evalidPix=erod (validPix, 3). In evalidPix, find the top rowcontaining the first valid pixel, topR; find the bottom row containingthe last valid pixel, botR; the middle row is expressed asmidR=(topR+botR)/2; then, set all evalidPix pixels above midR to 0.Compute mean, mean, and standard deviation, stdDev, of sRed on theregion defined by evalidPix. The best dynamic threshold is thenT=max(10, min (mean−1.5*stdDev, 80)). Threshold sRed using T in step1904.

[0651] Next, the method 1900 in FIG. 98 includes thresholding sRed usingT in step 1904. Then, step 1906 is performing a binary componentlabeling using 4-way connectivity. Finally, step 1908 is computing thearea of each object obtained in the previous step and selecting thelargest object. Flood fill the background of the object selected in theprevious step to fill holes. The result is the [ROI]_(vid) mask.

[ST]_(vid)

[0652] Step 1450 in FIG. 74 depicts the determination of a smoke tubemask, [ST]_(vid), for an image of a tissue sample. The smoke tube maskdetermines whether the smoke tube portion of the speculum used in theprocedure is showing in the image of the tissue sample. The smoke tubemask also identifies a portion of tissue lying over the smoke tube(which may also be referred to as “smoke tube” tissue) whose opticalproperties are thereby affected, possibly leading to erroneoustissue-class/state-of-health characterization. FIG. 99A depicts anexemplary image 1932 of cervical tissue used to determine acorresponding smoke tube mask, [ST]_(vid), 1934 shown in FIG. 99B. Thesmoke tube mask is determined in part by isolating the two “prongs”holding the smoke tube tissue. The two prongs are visible in the image1932 of FIG. 99A at reference numbers 1930 and 1931. In some images, theprongs are not visible. However, the smoke tube tissue in these images(without visible prongs) is generally either a blue or blue-green colorwith almost no red component; and the smoke tube in these images isidentified (and removed from consideration) by the generalregion-of-interest image mask, [ROI]_(vid).

[0653]FIG. 100 is a block diagram 1938 depicting steps in a method ofdetermining a smoke tube mask, [ST]_(vid), for an image of cervicaltissue. Image 1944 is an exemplary input image for which a correspondingsmoke tube mask 1960 is computed. Image 1944 shows a circle 1945 used insteps 1954 and 1956 of the method in FIG. 100.

[0654] The following describes the steps of the method shown in FIG.100, according to one embodiment.

[0655] The method 1938 in FIG. 100 comprises step 1946, pre-processingthe image. Pre-processing includes processing each RGB input channelwith a 3×3 median filter followed by a 3×3 boxcar filter to reducenoise. Step 1946 also includes calculating or retrieving the general ROImask ROImsk ([ROI]_(vid), described above) and the glare mask glareMsk(Glare_(vid), described above), and computing the search image, srcImg,as follows. First, compute the redness image Rn. Set to zero all valuesin Rn that are oustide ROImsk. Autoscale the redness image to the [0,1]range. Then, compute srchImg, which will be used at the final stages ofthe algorithm to compute a rough correlation to find the best circlelocation. SrchImg is a linear combination of the redness and red images:srchImg=(1−A)*Rn+A*R. The linear weight factor A is in the range [0.2,0.8]. Form validPix=ROImsk AND not(dil (glareMsk, 3). Compute mean,meanR, meanG, meanB of the RGB channels on the region defined byvalidPix. The weight A is initially computed as: A=max (0.5, min((2*meanR)/(meanG+meanB), 1.5)). Remap the value of A into the range[0.2, 0.8], A=0.2+(0.6*(A−0.5)). SrchImg is computed using the A factordetermined above.

[0656] Next, the method 1938 in FIG. 100 comprises a prong detectorfilter in step 1948. The prong detector is applied to the red image, Rand to an enhanced red image, RE to produce 2 different prong imagesthat will be arbitrated later. First, calculate the red-enhanced image,RE=R+max(R−G, R−B). Next, set up the prong detector filter. The filteris designed to be sensitive to smoke-tube prongs and to reject glare,edges and other features. The filter is a rectangular 35 by 15 separablefilter. The horizontal filter H is defined by H=[−1.5 −1.5 −1.5 −1.5−1.5 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 −1.5 −1.5 −1.5−1.5 −1.5]. The vertical filter V is a box car filter of length 15.Next, apply the prong filter to R and RE images yielding Rprong andReprong. Clip filtered images to 0 and autoscale to the range [0, 1].Set the bottom half of each filtered image as well as the first 20 andthe last 20 columns to 0 (there are no prongs in these sections ofimages). Then, find a maximum value for each of the first 125 rows ofthe 2 filtered images. Find the constant Rfact and REfact for eachfiltered image. These constants are defined as the mean of the maxima ofthe first 125 rows divided by mean of the first 125 rows. If(Rfact>Refact) use Rprong as the prong search image, iprong, otherwiseuse REprong.

[0657] Next, the method 1938 in FIG. 100 comprises thresholding,component analysis, and prong selection in step 1950. Step 1950 is usedto select prongs. First, threshold iProng image with a threshold of 0.2.Perform binary component labelling to obtain all regions (objects).Compute regions (objects) statistics, including area, centroid, andmajor and minor axis length. Filter prong regions (objects). Discardeach region (object) that statisfies any of the following criteria:

[0658] 1. Region size <300.

[0659] 2. iProng maximum on object <0.4.

[0660] 3. Region does not extend above row 100.

[0661] 4. Minor axis length >=30.

[0662] 5. Region does not overlap with ROImsk.

[0663] 6. Region centroid is below row 140.

[0664] 7. Centroid y-value >40 and object thinness (major axislength/minor axis length)<=2.

[0665] Choose as the main prong the brightest remaining region (i.ewhere the region maximum value is greater than the maxima from all otherremaining regions). Filter all other prong regions based upon thedistance from the main prong by calculating the distance from eachregion's centroid to the centroid of the main prong, and discarding theregion if the intra-centroid distance>160 or if the intra-centroiddistance<110.

[0666] Next, method 1938 in FIG. 100 comprises validation of theselected prongs in step 1952. For each retained prong object in step,1950, the following computations are peformed to validate the selectedprongs. Define pad, the rough distance from an object to its perimeter.Here, pad is set to 8. Form images of the dimension of the bounding boxof the object plus pad pixels on each side (OBJ). Crop the originalobject of the prong search image IOrig, from the original unsmoothed redchannel image, Rorig, and form the binarized image BWProng. Computeinternal region, intReg=erod (dil (OBJ, 2), 1). Compute object perimeterregion, perObj=dil ((dil (OBJ, 2) AND not (OBJ)), 2). Compute mean andstandard deviation, mean and std, of the object on the interior region,intReg, and the mean, pmean, on the perimeter region perObj. Computeleft/right bias by computing locations of the center points of theobject top row and bottom row, drawing a line connecting those 2 pointsto divide the perimeter region, perObj, into 2 sections, calculating themean value of iProng on each of the half perimeter sections, LperMean,RperMean, and using the means to compute left/right biases, LRBias usingLRBias=max (LperMean/RperMean, RperMean/LperMean). Discard any objectswhere the following holds: (mean/pmean<1.4) OR (std>0.55) OR((LRBias>1.45) AND (mean/pmean<1.48)). If more than 2 prong candidatesare remaining, keep the one closest to the main prong. If no prongcandidates are left, the smoke tube mask is empty.

[0667] Next, method 1938 in FIG. 100 comprises template searching usingcircles in step 1954. Step 1954 is used to qualify regions as smoke tubecandidates. First, construct a binary mask, validCervix, of valid cervixpixel locations: by Computing or retrieving blood mask, bloodMsk,(Blood_(vid), described below); Computing or retrieving glare mask,glareMsk, (Glare_(vid), described above), then compute the bloodMskusing validCervix=ROImsk AND not(BWProng) AND not(dil (glareMsk, 3)) ANDnot(bloodMsk). Then, determine an x-coordinate value for the center,xCent, of the circle and radius, rad. For 2 prongs xCent is the halfpoint between centroids of 2 prongs and rad is the half distance bewteenprongs +5. For 1 prong, choose a default rad of 85 and do a left-rightsearch to decide wether the smoke tube is on the left or right. Thex-coordinate values, xCent, for each of the 2 search circles is thex-coordinate of the prong centroid +/−rad. The y-coordinate, yCent, isthe y-coordinate of the prong centroid. For each circle center (xCent,yCent), find all points within rad that are in validCervix and computethe regional mean from the redness image. Then, find all points outsiderad that are in validCervix and compute the regional mean from theredness image. Compute the contrast as the ratio of inner mean rednessto outer mean redness and select the circle with minimum contrast.Discard the previous circle if xCent is within rad/4 from the left orright edge of the image, since it cannot be a smoke tube. Then, use thesearch image, srchImg, to perform an up-down search on the y-coordinate,yCent, to determine the actual smoke tube location using thex-coordinate xCent computed named above in section 2. Repeat the searchwith the redness image Rn if the results are unsatisfactory. A minimumand maximum value for yCent, yCentMin and yCentMax are chosen asfollows:

[0668] 1. yCentMin=−rad+yProngBot; where yProngBot is the mean of thebottom-most points of the prong(s), or the bottom-most point for asingle prong.

[0669] 2. For two prongs, yCentMax=yProngBot−(0.75*rad) i.e. the circlecannot extend beyond ¼ rad below the bottom of the prongs.

[0670] 3. For one prong, yCentMax=min(yProngBot+rad/3, 150) i.e. thecircle can go quite past the end of the prong, but not below the 150throw of the image.

[0671] Three more points spaced (yCentMax−yCentMin)/4 apart are computedbetween yCentMax and yCentMin. The search algorithm uses a total ofyCent candidate points. For each yCent candidate, the inner/outercontrast for circles centered at (xCent, yCent) are computed usingsrchImg as follows:

[0672] 1. Find all points within rad that are in validCervix and computethe regional mean from srchImg.

[0673] 2. Find all points outside rad that are in validCervix andcompute the regional mean from srchImg.

[0674] 3. Compute the contrast as the ratio of the inner mean value ofsrchImg to the outer mean value of srchImg and select the circle withminimum contrast.

[0675] Check to see that at least one of the 5 contrast numbers is lessthan 1. If not, break out of the loop and proceed no further with thissearch. If at least one circle has contrast less than 1, choose theminimum and select a new set of five points centered around this oneusing the following steps:

[0676] 1. If the top or bottom point was the minimum, choose that point,the one below/above it, and three points evenly spaced in between them.

[0677] 2. If one of the three central points was the minimum, choosethat point with the ones immediately below and above it, and twoadditional ones centered in the two spaces that divide those three.

[0678] Using the new set of five points, go back to the computation ofthe inner/outer contrast for circles using srchImg, discussed hereinabove, and proceed in this way until the distance between the fivepoints is less than 3 pixels. When the distance between the points isless than 2 pixels, exit the loop and choose the yCent with the currentminimum contrast number as the final value of yCent for the circle. Thecontrast for the final circle must be less than 0.92 in order for thealgorithm to find a valid circle. If that is not the case, then thesearch algorithm is repeated with the pure redness image, Rn instead ofsrchImg, which was a mixture of R and Rn. If the Rn search produces anacceptable result with contrast less than 0.92, then this value of yCentis used and we can proceed. Otherwise, there is no suitable circle andthe segmentation mask will contain prongs but no circle.

[0679] Finally, method 1938 in FIG. 100 comprises producing the finalsmoke tube segmentation mask in step 1958. First, set the values of allpixels above the horizontal line inside the circle which is bisected bythe center to 1. This effectively casts a “shadow” straight upward fromthe bottom of the image, and creates the effect that the smoke tube iscoming straight down from outside of the image. The shadowed circle andprong images are combined to yield the final segmentation mask. Clean upany stray non-prongs by performing a flood-fill of “on” valued regionswith seeds in the first or thirtieth row of the image to select onlyobjects that touch the first or thirtieth row of the image.

OS_(vid)

[0680] Step 1460 in FIG. 74 depicts the determination of an os imagemask, OS_(vid), for an image of a tissue sample. The optical propertiesof the os region may differ from optical properties of the surroundingtissue. In the method 1438 of FIG. 74, the os image mask is used in softmasking to penalize data from interrogation points that intersect or lieentirely within the os region. FIG. 101A depicts an exemplary image 1964of cervical tissue used to determine a corresponding os image mask,OS_(vid), 1968, shown in FIG. 101B.

[0681] The OS_(vid) image mask is determined using a combination ofthresholds from different color channels and using a binary componentanalysis scheme. An initial mask is formulated from a logicalcombination of masks computed from each color channel, R, G, B, andluminance, Y (equation 94). The four individual masks are computed usinga thresholding method in which the threshold is set relative to thestatistics of the colorplane values on the image region-of-interest(ROI). A component analysis scheme uses the initial mask to detect an oscandidate area (object), which is validated.

[0682]FIG. 102 is a block diagram 1988 depicting steps in a method ofdetermining an os mask, OS_(vid), for an image of cervical tissue. Image1990 is an exemplary input image for which a corresponding os mask 2004is computed. The following describes the steps of the method 1988 shownin FIG. 102, according to one embodiment.

[0683] The method 1988 in FIG. 102 includes image preprocessing in step1992. Preprocessing includes computing luminance Y from RGB componentsY=0.299*R+0.587*G+0.114*B; smoothing RGB channels using 2 iterations ofa 3×3 box car filter; and computing a ROI mask, ROImsk, ([ROI]_(vid))using the method described herein above. Next, process the ROI mask byeroding ROImsk 14 times to obtain eROImsk=erod (ROImsk, 14). Computeannulus perimeter, annMsk: annMsk=dil ((eROImsk AND not erod (eROImsk,1)), 4). This is a thick closed binary image which traces the edge ofthe ROI, useful in closing the boundary around any os which might extendto the background. Remove glare in ROImsk by logically AND-ing ROImskwith the complement of the glare mask (obtain as described above) toobtain centerROImsk. Then, compute a mean and standard deviation of eachcolor channel (meanR, stdR, meanG, stdG, meanB, stdB, meanY, stdy) inthe region specified by the centerROImsk.

[0684] Next, the method 1988 in FIG. 102 includes thresholding toproduce an initial segmentation mask in step 1994. First, cut-offcenterROImsk around the annulus: centerROImsk=centerROImsk AND not(annMsk). Next, form a binary mask for each of the RGBY channels thatrepresents pixels that exist in centerROImsk and that satisfy thefollowing conditions:

[0685] 1. mskR=(R pixels such that R<(meanR−.0.40*stdR));

[0686] 2. mskG=(G pixels such that G<(meanG−.0.65*stdG));

[0687] 3. mskB=(B pixels such that B<(meanB−.0.75*stdB));

[0688] 4. mskY=(Y pixels such that Y<(meanY−.0.75*stdY)).

[0689] The resulting “initial” segmentation mask, msk, is then definedby:

[0690] msk centerROImsk AND mskR AND mskG AND mskB AND msky.

[0691] Next, the method 1988 in FIG. 102 includes performing a binarycomponent analysis in step 1996. This step breaks up the segmentationmask into multiple objects. First, perform binary component labeling onsegmentation msk. Remove all objects with size less than 125. Breakapart all objects with size greater than 10000. For each object greaterthan 10000 (thisObjMsk), do the following:

[0692] 1. Compute mean value meanR and meanY for the area selected bythisObjMsk in the red and luminance channels.

[0693] 2. Set a new threshold for red and Y as follows:

[0694] a. redT=0.90*meanR

[0695] b. lumT=meanY

[0696] 3. Break the object apart, or make it smaller to yield newObj,then complement thisObjMsk with the region that is not part of the newlybroken-up region:

[0697] newObj=thisObjMsk AND (R pixels such as R>=redT) AND (Y pixelssuch as Y>=lumT).

[0698] thisObjMsk=thisOBjMsk AND (not(newObj).

[0699] 4. Keep track of the original large image mask (thisObjMsk) thatproduces the smaller objects in step c. Create a large object maskIgObMsk for each thisObjMsk that is set to on for each large objectwhich was found.

[0700] Next, the method 1988 in FIG. 102 includes performing dilation,binary component analysis, and candidate selection in step 1998. Step1998 is performed to find candidates from the multiple binary objectsproduced in step 1996. First, dilate segMsk produced in step 1996 twiceto obtain bMsk=dil (segMsk, 2). Perform a component labeling on bMsk.Discard objects of size less than 125 or greater than 23,000. For eachremaining object, thisObjMsk, apply the following procedure to selectcandidates:

[0701] 1. Compute mean, intMeanR, intMeanY, and standard deviation,intStdR, intStdY for red and luminance channel pixel values inthisObjMsk.

[0702] 2. Dilate thisObjMsk 7 times to yield dThisObjMsk=dil(thisObjMsk, 7).

[0703] 3. Compute perimeter mask:

[0704] a. thisObjPerim=dil ((thisObjMsk AND not(erod (dThisObjMsk,1))),3).

[0705] 4. Compute mean, perMeanR, perMeanY, and standard deviation,perStdR, perStdY, for red and luminance channel pixel values inthisObjPerim.

[0706] 5. Compute the following indicators:

[0707] a. os brightness (osBright)=intMeanY/perMeanY.

[0708] b. Perimeter uniformity (perUnif)=perStdR/intStdR.

[0709] 6. An object is an os candidate if:

[0710] ((osBright<0.85) AND (perUnif<1.75)) OR

[0711] ((osBright<0.7) AND (perUnif<2.85) AND (part of object came fromlarge object as recorded in IgObjMsk).

[0712] Next, the method 1988 in FIG. 102 includes performing candidatefiltering and final selection in step 2000. The remaining os candidatesare processed as follows. First, discard large non-os objects at theperiphery of the cervix using the following algorithm:

[0713] 1. Define a binary image with a centered circular area of radius150.

[0714] 2. Discard the object if more than half of it is outside thecircle and if perUnif>0.9. This step is done by performing a logical ANDof the object with the circular mask, counting pixels and comparing tothe original size of object.

[0715] If the number of remaining objects is greater than 1, perform thefollowing loop for each object:

[0716] 1. Compute the centroid of the object, and compute the distanceto the image center

[0717] 2. Exit if either:

[0718] a. The distance to the center is less than 100 for all objects.

[0719] b. No object lies within 100 pixels of center and a single objectremains.

[0720] Discard the object with the highest perUnif, and go back to stepb. Finally, step 2002 of the method 1988 in FIG. 102 determines thefinal os mask by twice eroding the final mask obtained in step 2000.

Blood_(vid)

[0721] Step 1458 in FIG. 74 depicts the determination of a blood imagemask, Blood_(vid), for an image of a tissue sample. The presence ofblood may adversely affect the optical properties of the underlyingtissue. In the method of FIG. 74, the blood image mask is used in softmasking to penalize data from interrogation points that intersect or lieentirely within the blood regions. FIG. 103A depicts an exemplary image2008 of cervical tissue used to determine corresponding blood imagemask, Blood_(vid), 2012, shown in FIG. 103B.

[0722] In one embodiment, the Blood_(vid) image mask is similar to theOs_(vid) image mask in that it is determined using an initial maskformulated from a logical combination of masks computed from each colorchannel R, G, B and luminance, Y. However, the initial Blood_(vid) imagemask is formed as a logical “OR” (not “AND”) combination of the fourdifferent masks, each designed to capture blood with different colorcharacteristics. Blood may be almost entirely red, in which case the Redchannel is nearly saturated and the green and blue channels are nearlyzero. In other cases, blood is almost completely black and devoid ofcolor. In still other cases, there is a mix of color where the redchannel dominates over green and blue. In one embodiment, theBlood_(vid) mask identifies relatively large regions of blood, not inscattered isolated pixels that may be blood. The logical OR allowscombination of regions of different color characteristics into larger,more significant areas that represent blood. As with the OS_(vid) mask,the Blood_(vid) mask is formulated by thresholding the initial mask andby performing component analysis.

[0723]FIG. 104 is a block diagram 2032 depicting steps in a method ofdetermining a blood image mask, Blood_(vid), for an image of cervicaltissue. The following describes the steps of the method 2032 shown inFIG. 104, according to one embodiment.

[0724] The method 2032 in FIG. 104 includes image preprocessing in step2034. Peprocessing includes computing luminance Y from RGB componentsY=0.299*R+0.587*G+0.114*B, and computing the ROI mask, ROImsk,([ROI]_(vid)) using the method described hereinabove.

[0725] Next, the method 2032 in FIG. 104 includes mask formation viathresholding in step 2036. The following steps are used to produce aninitial segmentation mask. First, four preliminary masks are generatedto detect “likely” regions of blood, as follows:

[0726] 1. To catch blood which is almost completely red, mskAmskA=ROImsk AND (B pixels such as B<15) AND (G pixels such as G<15) AND(R pixels such as R>2*max(G,B)).

[0727] 2. To catch areas where red dominates over green and blue, mskB:mskB=ROImsk AND (R pixels such as R>G*3) AND (R pixels such as R>B*3).

[0728] 3. To catch really dark, almost black blood, mskC: mskC=ROImskAND (R, G, B pixels such as R+G+B<60).

[0729] 4. To catch dark, but not completely black blood, mskD:mskD=ROImsk AND (R, G, B pixels such as R+G+B<150) AND (R pixels such asR<100) AND (R pixels such as R>max(G, B)*1.6).

[0730] The final candidate segmentation mask, mskOrig, is computed asfollows: mskOrig=mskA OR mskB OR mskC OR mskD.

[0731] Next, the method 2032 in FIG. 104 includes object selection usingdouble thresholding in step 2040. The following steps are used to selectregions that are blood candidate regions. First, a seed mask, seedMsk,is made by eroding mskOrig twice. Then, to connect neighboring pixels,dilate mskOrig once, then erode the result once to obtain clMskOrig.Finally, to eliminate spurious pixels and regions that are not connectedto larger features, compute mask, msk, by performing a flood fill of“on” valued regions of clMskOrig with seeds in seedMsk.

[0732] Next, the method 2032 in FIG. 104 includes binary componentanalysis and object filtering in step 2042. Binary component labeling isperformed on msk to select blood regions. For each labeled object thefollowing steps are performed:

[0733] 1. The Object mask is set to 0. Upon validation, the object maskis turned ON.

[0734] 2. An interior object is found by shrinking it once (1 erosionstep) unless it disappears, in which case the algorithm reverts to theoriginal object prior to erosion.

[0735] 3. Dilate the object OBJ 5 times, compute its perimeter anddilate the perimeter 5 times:

[0736] ObjPer=dil ((OBJ AND not(erod (dil (OBJ,5), 1))), 3).

[0737] 4. For both the interior and perimeter objects, the mean andstandard deviation is found for the Red, Green, and Blue color-planeswithin the objects. The interior and perimeter mean luminance is foundas the average of the Red, Green and Blue means.

[0738] 5. Two indicators are calculated which will help in the decisionstep:

[0739] a. DarkBloodIndicator=(Perimeter Red mean)/(Interior Red mean).This number is high for dark or black blood because there is more red inthe perimeter than in the interior.

[0740] b. BrightBloodIndicator=((Perimeter Green Mean+Perimeter BlueBean)/Perimeter Red Mean)/((Interior Green Mean+Interior BlueBean)/Interior Red Mean). This number is large when the interior regionhas a much higher red content than green and blue as compared to theperimeter.

[0741] 6. If the following three conditions are met, the region isconsidered to be a “noisy” feature which is most likely near the edge ofthe cervix. This determination affects the decision rules to follow:

[0742] a. Interior mean Red<40

[0743] b. (Interior standard deviation of Red>Interior mean Red) OR(Interior standard deviation of Green>Interior mean Green) OR (Interiorstandard deviation of Blue>Interior mean Blue)

[0744] c. DarkBloodIndicator<5.

[0745] 7. The decision rules: If any of the following three rules aresatisfied, then this object is Blood. Otherwise it is not.

[0746] a. DarkBloodIndicator>2.5 AND not “noisy”;

[0747] b. BrightBloodIndicator>2.25 AND not “noisy”;

[0748] c. BrightBloodIndicator>2.25 AND DarkBloodIndicator>2.5 (in thiscase it doesn't matter if it's a “noisy”).

[0749] 8. If the object is blood, it is turned ON in the finalsegmentation mask.

[0750] Finally, the method 2032 in FIG. 104 includes determining thefinal blood mask in step 2044. Step 2044 includes performing aflood-fill of all objects in which the seed objects were found to beblood. This yields the final blood segmentation.

Mucus_(vid)

[0751] Step 1464 in FIG. 74 depicts the determination of a mucus imagemask, Mucus_(vid), for an image of a tissue sample. The presence ofmucus may affect the optical properties of the underlying tissue,possibly causing the tissue-class/state-of-health characterization inthose regions to be erroneous. In the method 1438 of FIG. 74, the mucusmask is used in soft masking to penalize data from interrogation pointsthat intersect or lie entirely within the mucus regions. FIG. 105Adepicts an exemplary image 2064 of cervical tissue used to determine acorresponding mucus image mask, Mucus_(vid), 2068 shown in FIG. 105B.

[0752] In one embodiment, the Mucus_(vid) image mask is a modified bloodimage mask, tuned to search for greenish or bright bluish objects. FIG.106 is a block diagram 2072 depicting steps in a method of determining amucus mask, Mucus_(vid), for an image of cervical tissue. The followingdescribes steps of the method 2072 shown in FIG. 106, according to oneembodiment.

[0753] The method 2072 in FIG. 106 includes preprocessing in step 2074.Preprocessing includes processing each RGB input channel with a 3×3median filter followed by a 3×3 boxcar filter to reduce noise. Then,calculate or retrieve the following masks:

[0754] 1. Glare mask (Glare_(vid)): dilate glare mask once to yieldglareMsk

[0755] 2. ROI mask ([ROI]_(vid)): ROImsk

[0756] 3. Blood mask (Blood_(vid)): bloodMsk

[0757] 4. os mask (Os_(vid)): osMsk

[0758] Compute a valid cervix pixels mask, validCervix, by AND-ing theROImsk with the complement of the other masks as follows:validCervix=ROImsk AND not(glareMsk) AND not(bloodMsk) AND not(osMsk).

[0759] Next, the method 2072 in FIG. 106 includes mask formation viathresholding and morphological processing in step 2076. The followingsteps are used to produce an initial mucus segmentation mask. First,calculate the means, meanR, meanG and meanB, for the RGB channels on thevalidCervix region. Compute the difference, RGgap between the red andgreen mean: RGgap=meanR−meanG. Create a binary mask, mskOrig, accordingto the following rule: mskorig=ROImsk AND (R,G,B pixels such as((2*G−R−B)>=(10−RGgap/3))). This rule selects regions where green issomewhat higher than either red or blue relative to the gap. Finally,process the binary mask with an opening morphological operator to obtainopMsk, as follows:

[0760] 1. Perform two erosions with a 3-by-3 disk structuring element.

[0761] 2. Perform one dilation with a 3-by-3 square structuring element.

[0762] 3. Perform one dilation with a 3-by-3 disk structuring element.

[0763] Next, the method 2072 in FIG. 106 includes object selection usingdouble thresholding in step 2080. The followings steps are used toselect objects from the initial segmentation mask by computation of seedpoints. First, a seed image, seedMsk, is computed by eroding opMsk 3times. Then, opMsk is dilated twice then eroded once. Objects in opMskare selected using seedMsk. For example, object I is selected at pointswhere opMsk and seedMsk intersect, then selMsk is defined as theresulting object selection mask.

[0764] Then, the method 2072 in FIG. 106 includes binary componentanalysis and object filtering in step 2082. The following steps areapplied to all objects selected in step 2080:

[0765] 1. Perform binary component labelling on all selected objects inselMsk.

[0766] 2. Set final segmentation mask to all 0's.

[0767] 3. Compute area for each object in selMsk and discard any objectwith an area less than 1000 pixels, update selMsk by removing discardedobjects

[0768] 4. Process all remaining objects in selMsk as follows (steps2084, 2086):

[0769] a. Compute mean and standard deviations of the red, green andblue smoothed images, meanR, meanG, meanB, stdR, stdG, stdB, for eachobject.

[0770] b. Compute the object perimeter for each object:

[0771] i. Binary object, binObj, is dilated 15 timesdilBinObj=dil(binObj, 15).

[0772] ii. Object perimeter is computed and then dilated:perBinObj=dil((dilBinObj AND not(erod (dilBinObj, 1)), 4).

[0773] c. Compute mean and standard deviations on each color channel, topmeanR, pmeanG, pmeanB, pstdR, pstdG, pstdB for each region's perimeter.

[0774] d. Compute six decision rule indicators:

[0775] i. Mucus Indicator 1: mucInd1=(meanG/pmeanG)*(pmeanR/meanR)

[0776] ii. Mucus Indicator 2:

[0777] mucInd2=(meanG/pmeanG)*(pmeanR/meanR)*(meanB/pmeanB)

[0778] iii. Green bright indicator: gBrightInd=3*meanG−meanR−meanB

[0779] iv. Local variation quotient:

[0780] locVarQuo=(stdR+stdG+stdB)/(psdfR+pstdG+pstdB)

[0781] v. Target laser Indicator:

[0782] targLasInd=(meanG*(pmeanR+pmeanB))/(pmeanG*(meanR+meanB))

[0783] vi. Blue not too bright indicator: bNotBrightInd if ((meanB >meanR) AND (meanB > meanG))   bNotBrightInd = (meanG − meanR)/(2 *abs(meanB   − meanG) else   bNotBrightInd = 10.

[0784] e. Object is not mucus object if the following holds:

[0785] (mucInd1<1.25) OR (mucInd2<1.5) OR (gBrightInd<100) OR

[0786] (bNotBrighInd<1) OR

[0787] (targLasInd>1.5) OR (locVarQuo>1.75).

[0788] f. If the object is selected as a mucus object, it is added tothe final mucus mask.

[SP]_(vid)

[0789] Step 1452 in FIG. 74 depicts the determination of a speculumimage mask, [SP]_(vid), for an image of a tissue sample. [SP]_(vid) isused in hard masking in the tissue characterization method 1438 of FIG.74. Here, data from the interrogation points that intersect the speculumare removed from consideration in the tissue-class/state-of-healthclassification steps. FIG. 107A depicts an exemplary image, 2098, ofcervical tissue used to determine the corresponding speculum image mask,[SP]_(vid), 2100, shown in FIG. 107B.

[0790] In one embodiment, the speculum image mask is determined byfinding circles near the bottom of the image. Projections of a number ofdifferent types of speculums resemble circles of different radii. In oneembodiment, two types of circle searches are used: an outer bottomsearch and an inner bottom search. The outer bottom search finds pointsnear the bottom edge of the general region-of-interest and inferscircles from these points. If multiple circles result, they areevaluated to find the one that best models the curvature at the bottomof the region-of-interest. A circle that models this curvature wellenough is used to form the speculum segmentation mask, [SP]_(vid).

[0791] If the outer bottom search does not produce a circle that modelsthe ROI curvature well enough, then another search is performed to finda circle that models the curvature of a speculum within the ROI. This isthe inner bottom search, and may be necessary where there is significantreflection of light from the speculum. In the inner bottom search, a setof angular projections is formed based on a best guess of the center ofcurvature from the outer circle search. The projections are thenanalyzed to find a significant intensity trough near the end of theprojections that agrees with the general expected location of a speculumat the bottom of the image. The projection analysis provides new pointswith which to model circles, and the resulting circles are evaluatedusing the image data to detect the presence of a speculum.

[0792]FIG. 108 is a block diagram 2112 depicting steps in a method ofdetermining a speculum image mask, [SP]_(vid), for an image of cervicaltissue. The following describes the steps of the method 2112 shown inFIG. 108, according to one embodiment.

[0793] The method 2112 in FIG. 108 includes image preprocessing in steps2114 and 2116. The following steps are used to preprocess the image usedin speculum mask computation. First, remove glare from the RGB image byperforming the following:

[0794] 1. Calculate or retrieve glare mask, glareMsk (Glare_(vid)).

[0795] 2. Dilate glareMsk 4 times to obtain dilGlareMsk.

[0796] 3. Filter the RGB values using dilGlareMsk to perform run-lengthboundary interpolation as follows:

[0797] a. Raster scan each row of dilGlareMsk to find all beginnings andends of pixel runs.

[0798] b. For each pixel P(x,y) in a given run specified by beginningpoint P(xb, y) and end point P(xe,y) in the intensity image, replaceP(x,y) by half the linearly interpolated value at P(x,y) from P(xb,y)and P(xe,y).

[0799] c. Raster scan each column of dilGlareMsk to find all beginningsand ends of pixel runs.

[0800] d. For each pixel P(x,y) in a given run specified by beginningpoint P(x, yb) and end point P(x,ye) in the intensity image, add toP(x,y) half the linearly interpolated value at P(x,y) from P(x,yb) andP(x,ye).

[0801] Then, smooth the RGB channels by filtering twice with a 5×5 boxcar filter. Finally, calculate or retrieve the ROI mask, ROImsk([ROI]_(vid)). Next, the method 2112 in FIG. 108 includes outer bottomcircle detection in step 2120. The outer bottom circle detection isdesigned to find the best circular segmentation matching the bottom ofROImsk. Step 2120 includes the following:

[0802] 1. Where width specifies the image width, compute the x-locationof 7 columns (defined by none the intervals C_(i)=i·width/10, where i=1to 9). The x-locations are used to determine y-values. The resultant(x,y) pairs are used to find different candidate circles.

[0803] 2. Four candidate circles—narrow, wide, left, and right—arecalculated from the x values using the following matrix:

[0804] a. Narrow circle: C3 C5 C7

[0805] b. Wide circle: C2 C5 C8

[0806] c. Left circle: C2 C4 C6

[0807] d. Right circle: C4 C6 C8

[0808] 3. The y-values are determined by scanning the y-axis, at a givenx-position, starting at the bottom, until an “on” pixel is encounteredin ROImsk. The same process is performed for 5 adjacent pixels to theright and left of the given x-position. The resulting 11 y-values areaveraged to obtain the y-value used for calculating circles at the givenx-position.

[0809] 4. For each set of x values defined by the rows in the matrixabove, the y values are computed as described above, and the resultingthree pairs of coordinates are used to determine a unique circleintersecting these 3 points.

[0810] 5. A candidate circle is retained if:

[0811] a. Radius R>250 AND

[0812] b. R<700 AND

[0813] c. The circle's center lies at a y value less than 240 (half theimage height).

[0814] Next, the method 2112 in FIG. 108 includes validation of theouter circle in step 2122. The following steps are used to validate theouter circle:

[0815] 1. If circles remain after the previous pruning, perform thefollowing evaluation procedure:

[0816] a. Compute candidate circle center, draw perimeter at givenradius and construct 2 offset regions from the drawn perimeter.

[0817] b. The average intensity values, meanTop and meanBot, arecalculated for each region on the red image.

[0818] c. The BotTopRatio is calculated as the ratio of meanTop tomeanBot.

[0819] i. The top region is centered 10 pixels above the perimeter ofthe circle, and is 7 pixels in height. For example, for a given (x0,y0)point on the perimeter, the vertical region at x0 comprises the pixelsin the range (x0, y0+10) to (x0, y0+10−7).

[0820] ii. Similarly, the bottom region is centered 10 pixels below theperimeter of the circle, and is 7 pixels in height.

[0821] d. The average intensity values, meanTop and meanBot, arecalculated for each region on the red image.

[0822] e. The BotTopRatio is calculated as the ratio of meanTop tomeanBot.

[0823] 2. The circle with the best fit to the actual speculum shouldminimize this ratio. If there is more than one circle remaining, thecircle with minimum BotTopRatio is chosen.

[0824] 3. If BotTopRatio>0.55, the circle is rejected, and it isconcluded that the outer bottom circle detection found no valid circle.

[0825] If BotTopRatio<0.55, the circle is kept as the initial result forthe speculum segmentation. If the outer circle detection produces acircle with a strong enough representation of the speculum, then this istaken as the result and an inner speculum search is not done. Otherwisethe inner speculum search is done. If no circle is found using the outeralgorithm, perform the inner bottom speculum search. If the outer searchfinds a circle, look at the BotTopRatio to determine whether itqualifies:

[0826] 1. If BotTopRatio<0.275, take the outer circle as the finalsegmentation mask and stop.

[0827] 2. If BotTopRatio>=0.275, try the inner speculum search to see ifit yields a satisfactory result.

[0828] Next, the method 2112 in FIG. 108 includes inner bottom circledetection in step 2126. The Inner bottom circle detection algorithmlooks for circles within the ROI mask by calculating angular projectionsand looking for “valleys” in the projections to determine points thatcan be used to infer circles. The resulting circles are evaluated with ascheme similar to the one for outer bottom circle detection. Step 2126includes the following:

[0829] 1. Angular projection center point selection:

[0830] a. If an outer circle was detected, use the center point of theouter circle.

[0831] b. Else, use the point (n/2,1), where n is the width of theimage.

[0832] 2. The inner speculum search is done on the red color-plane R anda redness-enhanced red image ERn. The search results from the two imagesR and ERn are evaluated as a group and the best result is taken from theentire set. The redness enhanced red image is given by ERn=(2*R+Rn)/3,where Rn is the redness image defined in Equation 95. If no innerspeculum is found from the redness enhanced red image, then the innerspeculum search has determined that there is no identifiable innerspeculum. The inner speculum search algorithm is described in thesubsequent steps.

[0833] 3. Calculate angular projections as follows:

[0834] a. Five x-values give the center of each projection as it crossesthe bottom row of the image: [C0 C2 C4 C6 C8].

[0835] b. From these x-values, the angle thetaCtr, the central angle forthe projection, is computed.

[0836] c. For each angle thetaCtr, a projection sweeping out 10 degrees(5 degrees on each side of thetaCtr) is calculated.

[0837] d. For each 10 degree span, 50 equidistant line profiles (10/50degrees) are used to calculate the projection. The profiles extend fromthe center point to the point where the line at each angle crosses thebottom row of the image.

[0838] e. The 50 profiles are averaged to yield the projection for eachof the angles thetaCtr.

[0839] f. Each projection profile is filtered with a 15 sample longboxcar moving window averager.

[0840] 4. Each projection is searched backward to find the first “peak”in the projection, then search backwards again until the valley beyondthat peak is found. This valley usually occurs near the boundary betweenthe speculum and the cervix. Not every projection will yield a goodvalley point V. The criteria for finding the valley V of a projection Pare as follows:

[0841] a. P (V)<=mean(P (V+k) for all k in [1:12] (12 samples after V);

[0842] b. P (V)<=mean(P (V+k) for all k in [−12:−1] (12 samples beforeV);

[0843] c. P (V)<=P (V+k) for all k in [−12:12];

[0844] d. P (V)<P (V+k)−4 for some k in [V:length(P)] (peak-valley is>=4);

[0845] e. For valley V, find the y coordinate value Y_(v) and check thaty_(v)>300.

[0846] 5. After V is located, search backwards to find the point VMinwhere the first derivative of the projection is less than K*minSlope,where minSlope is the minimum slope between the valley V and the maximumof P(n) for n in [1:V], and K is a constant parameter set to 0.3. VMinbecomes the final point used for inferring circles from this projection.

[0847] 6. If the number of points to infer circles (calculated from thevalleys as described above) is greater than 3, then as many circles aspossible can be identified from these points and evaluated. The circlesare chosen from the following matrix:CircleIDX = [1  3  5;  %  X − X − X  2  3  4;  %   − XXX−    1  2  3;  %  XXX − −  3  4  5;  %   − −XXX  1  2  4;  %  XX − X −   2  4  5;  %   − X − XX  1  3  4;  %  X − XX −   2  3  5;  %   − XX − X  1  2  5;  %  XX − −X  1  4  5];  %  X − −XX

[0848]  where the elements of the matrix correspond to the fiveprojections computed above. If a specific projection j fails to yield anacceptable valley point, then all rows of the CircleIDX matrix whichcontain j arc removed.

[0849] 7. All remaining rows in CircleIDX are used to select projectionsfor inferring circles. The circles are calculated by first getting (x,y) coordinates for the 3 points defined in the steps above, using thecenter of projection and the radius along the projection. A uniquecircle is fitted through the 3 points, unless points are collinear, andcircle center (xCent, yCent) and radius rad are computed.

[0850] Next, the method 2112 in FIG. 108 includes validation of theinner bottom circle in step 2128. The following steps are used tovalidate the inner bottom circle:

[0851] 1. For each circle, the circle is discarded if any of thefollowing conditions applies:

[0852] a. rad<250 (the circle is too small to be a speculum)

[0853] b. yCent>(image height)/2 (center of circle in lower half ofimage or beyond).

[0854] 2. Each remaining circle is evaluated with the followingtechnique:

[0855] a. A temporary image is defined for identifying three differentregions specific to the circle. It is an 8-bit image with the followingvalues:

[0856] i. 1 for the “inner” region, which is the region between thecircle and another circle whose center is 12 pixels below the originalone.

[0857] ii. 2 for the “bottom” region, which is a 12 pixel wide circledrawn centered at 20 pixels below the original circle.

[0858] iii. 3 for the “top” region, which is a 12 pixel wide circledrawn centered at 20 pixels above the original circle.

[0859] iv. 0 for all other points in the image.

[0860] b. Five sets of pixels are calculated on the temporary image. Theaverage pixel value is calculated from the search image (Red or Rednessenhanced Red) for each set of pixels:

[0861] i. Top pixels, used to calculate AvgTop;

[0862] ii. Bottom Pixels, used to calculate Avg Bot;

[0863] iii. Inner pixels, used to calculate AvgIn;

[0864] iv. Outer pixels (top and bottom), used to calculate AvgOut;

[0865] v. Inner-bottom pixels (inner and bottom), used to calculateAvgInBot.

[0866] c. Two ratios are calculated from these sets of pixels:

[0867] v. InOutRatio=AvgIn/AvgOut;

[0868] vi. BotTopRatio=min([AvgBot/AvgTop, AvgIn/AvgTop,AvgInBot/AvgTop]).

[0869] d. The InOutRatio gives an estimate of how closely the circleconforms to a low-intensity cervix-speculum boundary, and theBotTopRatio helps to evaluate how well the circle matches an intensitydifference.

[0870] e. To be a valid speculum representation, a circle should satisfythe following criterion:

[0871] (InOutRatio<0.70) OR (InOutRatio<0.92 AND BotTopRatio<0.83).

[0872] If no circles meet this criterion, then the algorithm detects NOinner speculum.

[0873] f. The inner circle representing the speculum is the circle fromstep e that has the minimum value of InOutRatio.

[0874] g. If there is a resulting circle that has passed the validationprocedure, evaluate to verify it is not a false positive by comparingthe mean luminance on two portions of the ROI, above the speculum andbelow the speculum.

[0875] vii. Glare, blood and os are removed from ROI to obtain dROI,where

[0876] dROI=ROI AND not(glareMsk) AND not(bloodMsk) AND not(osMsk).

[0877] viii. Compute mean luminance, meanLTop, on dROI region abovecircle.

[0878] ix. Compute mean luminance, meanLBot, on dROI region belowcircle.

[0879] x. If meanLBot>0.8*meanLTop and the bottom-most point on theinner circle is less than ¾ of the image height, then the candidate is afalse positive and is discarded.

[0880] Finally, the method 2112 in FIG. 108 includes final determinationof the specular segmentation mask in step 2128. The final segmentationmask is computed from the results of the inner and outer speculumsearches. If the outer search produces a satisfactory result and noinner search is done, the final mask is the one computed by the outerspeculum search. If the outer search produces a satisfactory result andan inner search is performed which also produces a result, the finalsegmentation mask is the logical OR of the inner and outer masks. If theouter search produces no result but the inner search produces a result,the final mask is the mask from the inner search. If neither searchproduces a result, the final segmentation is empty, indicating that thealgorithm has determined that no speculum is present.

[VW]_(vid)

[0881] Step 1454 in FIG. 74 depicts the determination of a vaginal wallimage mask, [VW]_(vid), for an image of a tissue sample. [VW]_(vid) isused in hard-masking in the tissue characterization method 1438 of FIG.74. FIG. 109A depicts an exemplary image 2190 of cervical tissue used todetermine the corresponding vaginal wall image mask, [VW]_(vid), 2194shown in FIG. 109B.

[0882] In one embodiment, the vaginal wall mask detects vaginal wallsand cervical edges, including formices and speculum blades. Here, themask is determined using a filter shaped like a notch to emphasize thevaginal wall. This is similar to template matching in which the templateis present along one dimension and the filter is constant along theother dimension. This achieves a projection-like averaging.

[0883] After application of the filter in horizontal and verticalorientations, the resultant gradient images are thresholded andskeletonized. A heuristic graph searching method connects disconnectededges, and the edges are extended to the bounds of the image to form afull mask. Once the edges are extended, the edge lines are shadowedoutward from the center of the image to form the final vaginal wallsegmentation mask, [VW]_(vid).

[0884]FIG. 110 is a block diagram 2218 depicting steps in a method ofdetermining a vaginal wall image mask, [VW]_(vid), for an image ofcervical tissue. The following describes the steps of the method 2218shown in FIG. 110, according to one embodiment.

[0885] The method 2218 in FIG. 110 includes preprocessing in step 2220.First, calculate or retrieve the glare, glareMsk, ROI, ROIMsk, and os,osMsk, segmentation masks. Calculate the luminance L from the RGB signalusing the formula: L=0.299*R+0.587*G+0.114*B. Dilate glareMsk 4 times toobtain dilGlareMsk. Then, filter the RGB image using dilGlareMsk toperform run-length boundary interpolation as follows:

[0886] 1. Raster scan each row of dilGlareMsk to find all beginnings andends of pixel runs.

[0887] 2. For each pixel P(x,y) in a given run specified by beginningpoint P(xb, y) and end point P(xe,y) in the intensity image, replaceP(x,y) by half the linearly interpolated value at P(x,y) from P(xb,y)and P(xe,y).

[0888] 3. Raster scan each column of dilGlareMsk to find all beginningsand ends of pixel runs.

[0889] 4. For each pixel P(x,y) in a given run specified by beginningpoint P(x, yb) and end point P(x,ye) in the intensity image, add toP(x,y) half the linearly interpolated value at P(x,y) from P(x,yb) andP(x,ye).

[0890] 5. Perform a 11×11 box car filter smoothing on dilGlareMskregions only.

[0891] Finally, smooth the filled RGB channels by filtering once with a3×3 box car filter.

[0892] Next, the method 2218 in FIG. 110 includes gradient imageprocessing in steps 2222, and 2224. First, create a notch filter fordetecting the vaginal wall. The filter of length 22 is defined by thefollowing coefficients: [1 1 1 1 2/3 1/3 0 −1/3 −2/3 −1 −1 −1 −1 −2/3−1/3 0 1/3 2/3 1 1 1 1]. Then, normalize the filter: The average of thefilter coefficients is subtracted from the filter in order to make it azero-gain convolution kernel. Replicate rows 24 times to create a 22 by24 filter. Filter the luminance image L with the vaginal wall notchfilter to produce the vertical gradient image vGradImg. Filter theluminance image with the transpose of the notch filter to produce thehorizontal gradient image hGradImg. Clip gradient images to 0. Finally,perform the following thresholding and clean-up operations on each ofthe gradient images hGradImg and vGradImg:

[0893] 1. Threshold the images at 975 to yield a binary object image.

[0894] 2. Perform a binary component labeling using 4-way connectivity.

[0895] 3. Compute regions statistics: area, centroid, major and minoraxis length.

[0896] 4. Discard any object whose size is less than 1000 pixels.

[0897] 5. Discard any object which is within 80 pixels of distance fromthe center of the image.

[0898] 6. Dynamically calculate the minimum allowable length,

[0899] MinAllowedLength, for each object based upon the distance of itscentroid (xCentroid, yCentroid) from the center of the image (Cx, Cy)defined by Cx=(image width)/2 and Cy=(image height)/2. Let x be thedistance of the centroid to the center of the image, x=sqrt((xCentroid−Cx)²+(yCentroid−Cy)²).

[0900] MinAllowedLength scales the minimum allowed distance from 250 (atthe image center) to 100 at the left or rightmost edge of the image andis defined by:

[0901] MinAllowedLength=250−(15*x/25).

[0902] 7. Discard any object with a major axis length less than

[0903] MinAllowedLength.

[0904] 8. Discard any object that is more than 50% outside of theimage's ROI.

[0905] 9. Discard any object that covers more than 5% of the os.

[0906] Next, the method 2218 in FIG. 110 includes skeletonization instep 2226. The binary images resulting from step 2224 are processed witha skeletonization algorithm that approximates the medial axis transform.The skeletonization algorithm works for either horizontal or verticaledges. For vertical edges, each row is scanned from left to right. Eachtime the pixel values transition from OFF to ON, the index of the ONpixel is remembered. If the first pixel in the row is ON, this qualifiesas a transition. When there is a transition from ON back to OFF, theindex of the last ON pixel is averaged with the index from the previousstep to give the center pixel in the ON region. If an ON region extendsto the last pixel in the row, then this last pixel is treated as atransition point. All pixels between and including the first and last ONpixels are turned off except the center pixel. For horizontal edges,each column is scanned from top to bottom. The same steps describedhereinabove are repeated for the columns instead of the rows.

[0907] Next, the method 2218 in FIG. 110 includes edge linking andextension in steps 2226, and 2228. The skeletonizations are processedwith a heuristic graph-searching method which connects slight gaps inthe skeletonized images and extends the edges to the image boundary. Thefollowing images and parameters are used by the edge linking algorithm:

[0908] Horizontal and vertical skeletonized edge image, vSkelImg,hskelImg

[0909] Input label matrix, LbIMat. This is found by labeling matrixoutput from the connected components analysis, where discarded regionshave been removed from the label matrix by setting their pixel valuesback to 0.

[0910] Horizontal and vertical edge orientation, vEdgeOrient,hEdgeOrient.

[0911] Skeletonized input label matrix, skLblMat. This is a copy ofLblMat where all the pixels which are OFF in the skeletonized image areset to 0 in skLblMat.

[0912] Gap=16.0, the maximum allowable gap to fill in for a disconnectededge.

[0913] The following are searching methods that are implemented.

[0914] 1. Search for Edge Pixels: For both the horizontal and verticaledge images, the images are raster searched to locate edges within them.

[0915] a. The vertical edge image, vSkelImg, is searched by row rasterscanning to ensure that the first point in an edge is encountered.

[0916] b. The horizontal edge image, hsSkelImg, is searched by columnraster scanning to ensure that the first point in an edge isencountered.

[0917] c. When a point is encountered, the algorithm references skLblMatto see if that point has a positive label, indicating that this edge hasnot yet been processed. If so, the edge connection and edge extensionroutines described in the steps below are executed starting from thispoint.

[0918] 2. Edge Connection. The edge connection routine starts from thepoint from which it is called. The routine keeps a list of the pointsencountered in the edge. The search is executed only for points with thesame label in dilGlareMsk.

[0919] a. Create Label matrix skLblMat as described above.

[0920] b. Find second point:

[0921] i. Starting from the first point, do a search in a rectangularregion of size 2*(Gap+1.5)+1 centered about the first point.

[0922] ii. The second point will be the point which is ON in the edgeimage which is closest to the first point, and which is not already partof any other linked edge (must have same label value as the firstpoint).

[0923] iii. Fill in the gap between the first point and the secondpoint. The Gap filling algorithm is described below in step 3.

[0924] iv. If this edge begins at a point “sufficiently close” (withrespect to Gap) to another edge, set a flag to prevent extension of thebeginning of this edge.

[0925] v. If no second point is found, or if the second point is part ofanother edge which has already been linked, erase this edge in theoutput edge image (see Edge Erasing description below) and in skLblMat,stop processing this edge, and continue the loop to look for the nextedge.

[0926] c. Find the third point:

[0927] i. Starting from the second point, do a search in a rectangularregion of size 2*(Gap+1.5)+1 centered about the second point.

[0928] ii. The third point will be the point which is ON in the edgeimage which is closest to the second point, and which is not alreadypart of this or any other linked edge (must have same label value as thefirst point).

[0929] iii. Fill in the gap between the second point and the thirdpoint.

[0930] iv. If no third point is found, or if the third point is part ofanother edge which has already been linked, erase this edge in theoutput edge image, stop processing this edge, and continue the loop tolook for the next edge.

[0931] d. After three points in this edge are discovered, there isenough information to infer a search direction, and from here on out allsearches in the Edge Connection are directional. Steps for computing thesearch location are listed below.

[0932] e. Starting with the search for the fourth point, the followingsteps are iteratively performed until no further pixels in this edge canbe found:

[0933] i. The search direction: North (N), South (S), East (E), West(W), NorthEast (SE), NorthWest (NW), SouthEast (SE) or SouthWest (SW) iscomputed by the steps described below.

[0934] ii. Check the edge length, if it is greater than 2048, break outof the loop because this edge must have looped back upon itself.

[0935] iii. Find the next point in the given search direction: If nofurther points were found, check to see if the edge length is less than120.

[0936] 1. If edge length <120, erase edge and break out of this loop tocontinue the processing to find other edges (back to step 1).

[0937] 2. If edge length >=120, keep edge end break out of loop andcontinue with step f).

[0938] iv. Fill in the gap between the current point and the new point.

[0939] v. If the new point belongs to an edge which was already linkedby this algorithm, do the following:

[0940] 1. If the current edge is less than 40 pixels in length, erasethis edge. Break out of the loop and continue searching for furtheredges (back to step 1).

[0941] 2. Otherwise, the edge will be kept, but a flag is set so thatthe end of this edge is not extended. Break out of the loop and continuewith step f.

[0942] vi. Increment the edge length so that the new point becomes thecurrent point for the next iteration.

[0943] vii. Continue with step i) to continue processing.

[0944] f. At this point, a valid edge has been detected. This edge willthen be extended in the both directions to the boundary of the imageunless either edge (or both) is flagged for not extending. The edgeextension steps are described below in step 5.

[0945] g. Check to see if an extension passed through the center of theimage (defined by a circle of radius 80 centered at the geometricalcenter of the image).

[0946] i. If an extension did pass through the center of the image,erase this edge and all of its extensions.

[0947] ii. Otherwise, relabel this edge in the Label matrix to havevalue −1, and draw the extensions on the output edge image,simultaneously labeling the corresponding pixels in the Label matrixwith value −2.

[0948] 3. Gap Filling method:

[0949] a. Check to see if there is no gap, i.e. if the edge is alreadyconnected. Where (x1,y1) and (x2,y2) are the new point and the currentpoint, if abs(x1−x2)<2 and abs(y1−y2)<2, then there is no gap to fill,and the Gap Filling processing stops.

[0950] b. Remove the. “New pixel” from the edge vectors so that it canbe replaced with a set of filled-in pixels.

[0951] c. Check for special cases where x1=x2 or y1=y2. In either ofthose two cases, the Gap Filling is accomplished by simply turning onevery pixel which lies between the two pixels in the output Edge image.

[0952] d. For the case where x1 is not equal to x2 and y1 not equal toy2, a diagonal line needs to be drawn to fill the gap.

[0953] i. This is done first by computing an equation for the line whichconnects the two points.

[0954] ii. If the slope is greater than 1, iterate from y=y1 to y2, andcompute the x value for each y value. For each (x,y) turn on thecorresponding pixel in the output Edge image and in skLabMat.

[0955] iii. If the slope is less than 1, iterate from x=x1 to x2, andcompute the y value for each x value. For each (x,y) turn on thecorresponding pixel in the output Edge image and in skLabMat.

[0956] e. Finally, all of the new pixels are added to the edge vectorsin order from the current pixel to the new one. The corresponding pixelsin skLabMat are set to the label value −2.

[0957] 4. Computing Search Direction:

[0958] a. Two pixel locations are used to infer a search direction.

[0959] i. The first point is the geometric average of the two mostcurrent pixels in the edge.

[0960] ii. If there are less than 6 pixels in the edge, the second pointis the average of the first and second pixels in the edge.

[0961] iii. If there are more than 6 pixels in the edge, the secondpoint is the average of the fifth and sixth most current pixels in theedge.

[0962] b. For the two pixels (x1,y1) and (x2,y2), the search directionis computed as follows:

[0963] i. Compute the angle formed by the two points using the ATAN2function:

angle=atan2(y1−y0,x1−x0)*180/π;

[0964] ii. If angle is in the interval [−22.5, 22.5], the searchdirection is E.

[0965] iii. If angle is in the interval [22.5, 67.5], the searchdirection is SE.

[0966] iv. If angle is in the interval [67.5, 112.5], the searchdirection is S.

[0967] v. If angle is in the interval [112.5, 157.5], the searchdirection is SW.

[0968] vi. If angle is in the interval [−67.5, −22.5], the searchdirection is NE.

[0969] vii. If angle is in the interval [−112.5, −67.5], the searchdirection is N

[0970] viii. If angle is in the interval [−157.5, −112.5], the searchdirection is E.

[0971] ix. Otherwise, the search direction is W.

[0972] 5. Edge Extension:

[0973] a. It is the default to extend both the beginning and end of theedge. However, during the edge connection steps, if it is discoveredthat the edge originates close to a different edge, the edge isconnected to the different edge and is not extended. If an edge ends bymerging with another edge, the end of the edge is not extended.

[0974] b. For both the beginning and the end of the edge:

[0975] i. For Vertically oriented edge images (vEdgeOrient):

[0976] 1. If the y-coordinate for the first/last point of the edge isless than the image height/6 or greater than 5*height/6, extend thebeginning/end of the edge using the local slope method (describedbelow).

[0977] 2. Otherwise, extend the beginning/end of the edge using theglobal slope method (described below).

[0978] ii. For Horizontally oriented edge images (HEdgeOrient):

[0979] 1. If the x-coordinate for the first/last point of the edge isless than the image width/6 or greater than 5*width/6, extend thebeginning/end of the edge using the local slope method (describedbelow).

[0980] 2. Otherwise, extend the beginning/end of the edge using theglobal slope method (described below).

[0981] c. Local Slope Extension: This method uses the slope of the edgenear its beginning/end to determine the slope of the extending line.

[0982] i. Compute two points for slope computation:

[0983] 1. the average of the four pixels from the beginning/end of theedge; and

[0984] 2. the average of the 6th through 9th pixels from thebeginning/end of the edge.

[0985] ii. Using the two computed points, the edge is extended from itsbeginning/end point using a line of the computed slope until it reachesthe edge of the image.

[0986] d. Global Slope Extension: this method uses pixel values between20% and 80% of the length along the edge to guess the “average” slope ofthis edge. Then the beginning/end of the edge is extended using thisslope.

[0987] i. If the edge has edgeLen pixels in it, select the points in theedge with the following indices:

[0988] 1. begIDX=round(edgeLen*0.2); pointA=edge(begIDX);

[0989] 2. endIDX=round(edgeLen*0.8); pointB=edge(endIDX).

[0990] ii. Compute the slope using pointA and pointB, and use a line ofthis slope to extend from the beginning/end of this edge.

[0991] e. After the extension is computed, the extended pixels areturned ON in the output edge image, and the corresponding pixels inskLabMat are assigned value −2.

[0992] 6. Edge Erasing.

[0993] When an edge is to be erased check to verify that for each pixelin the edge and its extension the label for the pixel is >0. If so, setthe value in the output Edge image and the label matrix to 0. Thismethod assures that pixels in another edge that has already been linkedare not erased (the two edges might have crossed).

[0994] Finally, the method 2218 in FIG. 110 includes mask computation instep 2230. The output of the Edge Linking algorithm is used to generatethe vaginal wall mask in the following way:

[0995] 1. Vertical connected-edge image: VConnImg, a cumulative sum, iscalculated for each row, starting from the center and extending both tothe left and to the right.

[0996] 2. Horizontal connected-edge image: HConnImg, a cumulative sum,is calculated for each column, starting from the center and extendingboth upward and downward.

[0997] 3. The two cumulative sums are thresholded at >=1 and OR-edtogether to yield the final vaginal wall mask.

[FL]_(vid)

[0998] Step 1454 in FIG. 74 depicts the determination of afluid-and-foam mask, [FL]_(vid), for an image of a tissue sample. Thismask identifies fluid and foam regions appearing on tissue samples andis used in hard masking in the tissue characterization method 1438 ofFIG. 74. FIG. 11 A depicts an exemplary image 2234 of cervical tissueused to determine the corresponding fluid-and-foam image mask,[FL]_(vid), 2238 shown in FIG. 111B.

[0999] In one embodiment, the fluid-and-foam image mask identifiesregions where excess fluids and/or foam collect on cervical tissue.Excess fluid or foam can collect near the speculum, around or in the os,and/or in the folds between the vaginal walls and the cervix, forexample. One embodiment of the fluid-and-foam image mask, [FL]_(vid),uses a measure of whiteness and a measure of blue-greenness to identifyregions of fluid/foam. After extracting white and blue-green colorfeatures, thresholding and validation is performed to produce the finalfluid-and-foam image mask, [FL]_(vid).

[1000]FIG. 112 is a block diagram 2258 depicting steps in a method ofdetermining a fluid-and-foam image mask, [FL]_(vid), for an image ofcervical tissue. The following describes the steps of the method 2258shown in FIG. 112, according to one embodiment.

[1001] The method 2258 in FIG. 112 includes preprocessing in step 2260.First, remove glare from the RGB image. Retrieve or compute glare mask,glareMsk. Dilate glareMsk 4 times to obtain dilGlareMsk. Next, retrieveor compute ROI mask, ROIMsk. Finally, smooth each of the RGB channelusing a 3×3 box car filter to remove noise.

[1002] Next, the method 2258 in FIG. 112 includes image color featurecalculation in step 2262. This step computes a “whiteness” image, Wimg,and a “green-blueness” image, GBImg. First, calculate the luminance Lfrom the RGB signal using the formula: L=0.299*R+0.587*G+0.114*B. Next,compute, normalize and threshold Wimg as follows:

[1003] 1. WImg=abs((R−G)/(R+G))+abs((R−B)/(R+B))+abs((G−B)/(G+B)).

[1004] This operation is a pixel-wise operation and is performed on eachpixel sequentially.

[1005] 2. Normalize Wimg: WImg=3−Wimg.

[1006] 3. Set low luminance pixels to 0 (low luminance pixels areunlikely to be in the fluid and foam regions):

[1007] If L<mean(L), WImg=0.

[1008] Finally, compute, normalize and threshold BGImg as follows:

[1009] 1.BGImg=(abs((R+30−G)/(R+30+G))+abs((R+30-−B)/(R+30+B))+abs((G−B)/(G+B))).

[1010] This operation is a pixel-wise operation and is performed on eachpixel sequentially.

[1011] 2. Normalize BGImg, BGImg=3−BGImg.

[1012] 3. Set low luminance pixels to 0 (low luminance pixels areunlikely to be in the fluid and foam regions):

[1013] If L<0.65*mean(L), BGImg=0.

[1014] Next, the method 2258 in FIG. 112 includes processing andsegmenting bright green-bluish regions in steps 2264, 2266, 2268, 2270,2272, 2274, and 2276. These steps are performed as follows:

[1015] 1. Retrieve or compute glare mask, glareMsk.

[1016] 2. Fill glare regions of BGImg using glareMsk to performrun-length boundary interpolation as follows:

[1017] a. Raster scan each row of glareMsk to find all beginnings andends of pixel runs.

[1018] b. For each pixel P(x,y) in a given run specified by beginningpoint P(xb, y) and end point P(xe,y) in the intensity image, replaceP(x,y) by half the linearly interpolated value at P(x,y) from P(xb,y)and P(xe,y).

[1019] c. Raster scan each column of glareMsk to find all beginnings andends of pixel runs.

[1020] d. For each pixel P(x,y) in a given run specified by beginningpoint P(x, yb) and end point P(x,ye) in the intensity image, add toP(x,y) half the linearly interpolated value at P(x,y) from P(x,yb) andP(x,ye).

[1021] 3. Eliminate low intensity areas using a threshold of 1.5:

[1022] If BGImg<1.5, BGImg=1.5.

[1023] 4. Rescale the BGImg to [0, 1]:

[1024] BGImg=BGImg−min(BGImg))/(3−min(BGImg).

[1025] 5. Compute thresholds from image statistics and performthresholding.

[1026] a. Compute image mean intensity, Imean, for BGImg>0.

[1027] b. Compute image standard deviation of intensity, IstdDev, forBGOImg>0.

[1028] Compute threshold thGB, thGB=,Imean+1.63*IstdDev.

[1029] c. Apply threshold limits:

[1030] if thGB<0.80, thGB=0.80;

[1031] if thGB>0.92, thGB=0.92.

[1032] d. Threshold to get the initial green-bluish fluid and foam maskGBMask if BGImg > thGB, then   GBMask = 1; else   GBMask = 0.

[1033] 6. Perform morphological processing to fill small holes andsmooth boundaries of the found regions in GBMask:

[1034] a. Dilate the segmentation mask GBMask twice, GBMask=dil(GBMask,2).

[1035] b. Erode the resultant mask three times, GBMask=erode(GBMask, 3).

[1036] c. Dilate the resultant mask once, GBMask=dil(GBMask, 1).

[1037] 7. Perform binary region labeling and small region removal:

[1038] a. Perform a connected components labeling, described above, tolabel all found regions.

[1039] b. Compute each region area, area, and eccentricity, ecc.

[1040] c. Remove small and round regions and small line segments thatare not likely to be the fluid and foam regions:

[1041] If ((area<1000) AND (ecc<0.70)) OR ((area<300) AND (ecc>0.70)) OR(area<1000), remove region.

[1042] 8. Green-Bluish feature validation for each found region is basedon the original RGB values:

[1043] a. For each found region, retrieve the mask, Imsk, and computethe mean intensities within the region for each of the red, green andblue channels as MRed, MGreen and Mblue.

[1044] b. If the found region is tissue-like, remove the region:

[1045] if [(MGreen−MRed)+(MBlue−MRed)]<−5 remove region.

[1046] c. If the found region is too blue, remove the region:

[1047] if (MBlue>MGreen+15) remove region.

[1048] 9. The final green-bluish fluid and foam mask, FGBMask, iscalculated by performing a flood-fill of “on” valued regions of GBMaskfrom step 5 with seeds in the validated regions from step 6 and step 7.

[1049] Next, the method 2258 in FIG. 112 includes processing andsegmenting pure white regions in steps 2278, 2280, 2282, 2284, 2286,2288, and 2290. These steps are performed as follows:

[1050] 1. Retrieve glare mask, glareMsk and ROI mask, ROIMsk.

[1051] 2. Fill glare regions of WImg using glareMsk to performrun-length boundary interpolation as follows:

[1052] a. Raster scan each row of glareMsk to find all beginnings andends of pixel runs.

[1053] b. For each pixel P(x,y) in a given run specified by beginningpoint P(xb, y) and end point P(xe,y) in the intensity image, replaceP(x,y) by half the linearly interpolated value at P(x,y) from P(xb,y)and P(xe,y).

[1054] c. Raster scan each column of glareMsk to find all beginnings andends of pixel runs.

[1055] d. For each pixel P(x,y) in a given run specified by beginningpoint P(x, yb) and end point P(x,ye) in the intensity image, add toP(x,y) half the linearly interpolated value at P(x,y) from P(x,yb) andP(x,ye).

[1056] 3. Compute WImg mean, mWImg, and standard deviation, stdWImg.

[1057] 4. Eliminate low intensity areas:

[1058] if WImg<mWImg−0.1*stdWImg, WImg=mWImg−0.1*stdWimg.

[1059] 5. Rescale the WImg to [0, 1]:

[1060] WImg=WImg−min(WImg))/(3−min(WImg).

[1061] 6. Compute thresholds from image statistics and performthresholding:

[1062] a. Compute image mean intensity, Imean, for WImg>0.

[1063] b. Compute image standard deviation of intensity, IstdDev, forWImg>0.

[1064] c. Compute threshold thW,

[1065] thW=Imean+1.10*IstdDev.

[1066] d. Threshold to get the initial green-bluish fluid and foam maskWMask: if ((Wlmg > thW) AND (pixel is included in ROIMsk)), then   WMask= 1; else   WMask = 0.

[1067] 7. Perform morphological processing to fill small holes andsmooth boundaries of the found regions in WMask:

[1068] a. Erode the segmentation mask WMask twice, WMask=erode(WMask,2).

[1069] b. Dilate the resultant mask three times, WMask=dilate(WMask, 3).

[1070] 8. Perform binary region labeling and small region removal:

[1071] a. Perform a connected components labeling, as described, tolabel all found regions.

[1072] b. Compute each region area, area.

[1073] c. Remove small regions that are not likely to the fluid and foamregions:

[1074] If (area<300) remove the region from the region list.

[1075] 9. Whiteness feature validation for each found region based onthe original RGB values:

[1076] a. For each found region, retrieve the mask, iMsk, and computethe mean intensities within the region for each of the red, green andblue channels as iMRed, iMGreen and iMBlue.

[1077] a. Dilate iMsk five times to obtain iD1Msk=dilate(iMsk, 5).

[1078] b. Compute the perimeter pixels iPeriMsk from iD1Msk:

[1079] iPeriMsk=not (erod (iD1Msk, 1)) AND (iD1Msk)), 1).

[1080] c. Dilate iPeriMsk three times to get the outer mask:iD2Msk=dilate (iPeriMsk, 3).

[1081] d. Compute mean intensities on iD2Msk for each of the R, G and Bchannels as perimeter (Outer) means: pMRed, pMGreen and pMBlue.

[1082] e. Compute the Inner region green-blueness:

[1083] innerGB=(iMGreen−iMRed)+(iMBlue−iMRed).

[1084] f. Compute the Inner region whiteness:

[1085]innerW=3.0−(abs((iMRed−iMGreen)/(iMRed+iMGreen))+abs((iMGreen−iMBlue)/(iMGreen+iMBlue))+abs((iMBlue−iMRed)/(iMBlue+iMRed))).

[1086] g. Compute the Outer region whiteness:

[1087]outerW=3.0−(abs((pMRed−pMGreen)/(pMRed+pMGreen))+abs((pMGreen−pMBlue)/(pMGreen+pMBlue))+abs((pMBlue−pMRed)/(pMBlue+pMRed))).

[1088] h. Compute the Outer region redness:

[1089] outerRed=(pMRed−pMGreen)+(pMRed−pMBlue).

[1090] i. Apply general whiteness validation rule:   if (((innerGB < 10)AND (outerRed > 25)) OR (outerW > (innerW − 0.1)), then:    set isFluidto 0, since it is not likely to be a fluid and foam region;   else,   Set isFluid to 1.

[1091] j. Very white fluid-foam validation rule:

[1092] If ((innerW>(outerW+0.16)) set is Fluid to 1.

[1093] k. Very high inner green bluish fluid-foam validation rule:

[1094] If (innerGB>10) set is Fluid to 1.

[1095] 10. The final white fluid-foam mask fWMask is calculated byperforming a flood-fill of “on” valued regions of Mask from step 8 withseeds in the validated regions (is Fluid=1) from step 9.

[1096] Finally, the method 2258 in FIG. 112 includes constructing thefinal fluid-foam mask. The final fluid-foam mask is a logical “OR” ofthe two segmented and validated masks as follows: FluidFoamMask=fBGMaskOR fWMask.

Classifiers

[1097] In one embodiment, the tissue characterization system 100 of FIG.1 comprises using broadband reflectance data obtained during a spectralscan of regions (interrogation points) of a tissue sample to determineprobabilities that a given region belongs in one or moretissue-class/state-of-health categories. In one embodiment,probabilities of classification are determined as a combination ofprobabilities computed by two different statistical classifiers. The twoclassifiers are a DASCO classifier (discriminant analysis with shrunkencovariances), and a DAFE classifier (discriminant analysis featureextraction). The DASCO classifier (step 1484, FIG. 74) uses a principalcomponent analysis technique, and the DAFE classifier (step 1482, FIG.74) uses a feature coordinate extraction technique to determineprobabilities of classification.

[1098] The embodiment shown in FIG. 74 applies a necrosis mask 1424 anda hard “indeterminate” mask 1426 to a set of arbitrated broadbandspectral data to eliminate the need to further process certain necroticand indeterminate interrogation points in the classification steps 1482,1484, 1486. After determining statistical classification probabilitiesin step 1486, the embodiment of FIG. 74 applies a soft “indeterminate”mask 1428 as well as the NED (no evidence of disease) classificationresult 1430 in order to obtain a final characterization 1432 of eachinterrogation point on the tissue sample as Necrotic, CIN 2/3, NED, orIndeterminate.

[1099] The statistical classifiers in steps 1482 and 1484 of FIG. 74each determine respective probabilities that a given region belongs toone of the following five tissue-class/state-of-health categories: (1)Normal squamous (N_(s)) (2) CIN 1 (C₁), (3) CIN 2/3 (C₂₃), (4)Metaplasia (M), and (5) Normal columnar (C_(ol)) tissue. Otherembodiments use one or more of the following tissue classes instead ofor in addition to the categories above: CIN 2, CIN 3, NED (no evidenceof disease), and cancer. The category with the highest computedprobability is the category that best characterizes a given regionaccording to the classifier used. In one alternative embodiment, othercategories and/or another number of categories are used. The results ofthe two statistical classifiers are combined with the NED maskclassification, along with the hard and soft “indeterminate” masks, toobtain a final characterization for each interrogation point 1432.

[1100] In one embodiment, statistical classification includes comparingtest spectral data to sets of reference spectral data (training data)representative of each of a number of classes. A collection of referencespectra from the same tissue class is a class data matrix. For example,a class data matrix T_(j)comprising reference spectra (training data)from samples having known class j is expressed as in Equation 96 asfollows: $\begin{matrix}{T_{j} = {\begin{bmatrix}{S_{1}\left( \lambda_{1} \right)} & {S_{1}\left( \lambda_{2} \right)} & \cdots & {S_{1}\left( \lambda_{p} \right)} \\{S_{2}\left( \lambda_{1} \right)} & {S_{2}\left( \lambda_{2} \right)} & \cdots & {S_{2}\left( \lambda_{p} \right)} \\\vdots & \vdots & \cdots & \vdots \\{S_{n_{j}}\left( \lambda_{1} \right)} & {S_{n_{j}}\left( \lambda_{2} \right)} & \cdots & {S_{n_{j}}\left( \lambda_{p} \right)}\end{bmatrix} \equiv \begin{bmatrix}{S_{1}(\lambda)} \\{S_{2}(\lambda)} \\\vdots \\{S_{n_{j}}(\lambda)}\end{bmatrix}}} & (96)\end{matrix}$

[1101] where class j contains n_(j) reference spectra, S(λ), and eachreference spectra, S(λ)=[S(λ₁),S(λ₂), . . . , S(λ_(p))], is ap-dimensional vector where p is the number of wavelengths in a measuredspectrum. The class data matrix T_(j) has associated with it a classmean vector μ_(j) (a 1-by-p vector) and a class covariance matrix C_(j)(a p-by-p matrix) as shown in Equations 97-99 as follows:$\begin{matrix}{{{\mu_{j}(\lambda)} \equiv \mu_{j}} = \left\lbrack {\frac{1}{n_{j}}{\sum\limits_{k = 1}^{n_{j}}\quad {{S_{k}\left( \lambda_{1} \right)}\quad \frac{1}{n_{j}}{\sum\limits_{k = 1}^{n_{j}}\quad {{S_{k}\left( \lambda_{2} \right)}\quad \ldots \quad \frac{1}{n_{j}}{\sum\limits_{k = 1}^{n_{j}}\quad {S_{k}\left( \lambda_{p} \right)}}}}}}} \right\rbrack} & (97) \\{C_{j} = {{\frac{1}{n_{j} - 1}{\sum\limits_{k = 1}^{n_{j}}{\left( \quad {{S_{k}(\lambda)} - \mu_{j}} \right)^{T}\left( {{S_{k}(\lambda)} - \mu_{j}} \right)}}}\quad \equiv {\frac{1}{n_{j} - 1}\left( {T_{j} - M_{j}} \right)^{T}\left( {T_{j} - M_{j}} \right)}}} & (98) \\{M_{j} = \begin{bmatrix}\begin{matrix}\begin{matrix}\mu_{j} \\\mu_{j}\end{matrix} \\\vdots\end{matrix} \\\mu_{j}\end{bmatrix}_{n_{j} \times p}} & (99)\end{matrix}$

[1102] Statistical tissue classification uses reference data todetermine for a given test spectrum to which class(es) and with whatprobabilit(ies) that test spectrum can be assigned.

[1103] The broadband data used in the statistical classifiers in steps1482 and 1484 are wavelength truncated. For the DASCO classifier (step1484), only training data and testing data that corresponds towavelengths between about 400 nm and about 600 nm are used. For the DAFEclassifier (step 1482), only training data and testing data thatcorrespond to wavelengths between about 370 nm and about 650 nm areused. One alternative embodiment uses different wavelength ranges. Thetraining data include reference broadband reflectance data frominterrogation points having a known classification in one of the fivestates of health, and the testing data include broadband reflectancedata from a region having an unknown classification.

[1104] The discriminant analysis feature extraction (DAFE) method ofstep 1482 in FIG. 74 transforms a measurement of high dimension into afeature space of lower dimension. Here, the feature space is theorthogonal projection in the direction of maximal data discrimination.The DAFE method includes constructing feature coordinates by computingthe feature space projection matrix. The projection matrix requires theinversion of the pooled within-groups covariance matrix, C_(pool). WhereT₁, T₂, . . . , T_(g) are training matrices for classes 1 through g(here, for example, g=5), the number of reference spectra in a givenclass, n_(j), may be less than the number of wavelengths in a measuredspectrum, p; and C_(pool), is therefore singular and cannot be inverted.

[1105] Thus, in one embodiment of the DAFE method of step 1482, thespectral measurements are subsampled so that a covariance matrix can becomputed. In one embodiment, a subsampling rate, n_(z), is determinedaccording to Equation 100: $\begin{matrix}{n_{z} = {{\max \left( \left\lfloor {\frac{p}{n_{1}},\frac{p}{n_{2}},\quad \ldots \quad,\frac{p}{n_{g}}} \right\rfloor \right)} + 1}} & (100)\end{matrix}$

[1106] where p is the number of wavelengths in a measured spectrum; n₁,n₂, . . . , n_(g) represent the numbers of reference spectra in each ofclasses 1, 2, . . . , g, respectively; and └ ┘ indicates the “nearestinteger” function. Typically, n_(z)=2 or 3, but values up to about 10 donot generally remove too much information from a measured reflectancespectrum, and may also be considered. After subsampling, thenon-singular pooled covariance matrix, C_(pool), is computed accordingto Equation 101 as follows: $\begin{matrix}{{C_{pool} = {\frac{1}{n - g}{\sum\limits_{k = 1}^{g}{\left( {n_{k} - 1} \right) \cdot C_{k}}}}}\quad {n = {\sum\limits_{k = 1}^{g}n_{k}}}} & (101)\end{matrix}$

[1107] where n_(k) is the number of reference spectra in class k; andC_(k) is the covariance matrix for class k. Then, the between-groupscovariance, C_(btwn), is computed according to Equation 102:$\begin{matrix}{{C_{btwn} = {\frac{1}{g}{\sum\limits_{k = 1}^{g}{{n_{k} \cdot \left( {\mu_{k} - \overset{\_}{\mu}} \right)^{T}}\left( {\mu_{k} - \overset{\_}{\mu}} \right)}}}}{\overset{\_}{\mu} = {\frac{1}{n}{\sum\limits_{k = 1}^{g}{n_{k} \cdot \mu_{k}}}}}{n = {\sum\limits_{k = 1}^{g}n_{k}}}} & (102)\end{matrix}$

[1108] Next, the maxtrix P=C_(pool) ⁻¹·C_(bwtn) is formed and singularvalue decomposition is applied to obtain the following:

P=UDV ^(T)  (103)

[1109] Let U_(g−1) equal the first g−1 columns of the orthogonal matrixof singular values U as follows: $\begin{matrix}{U = {\left. \begin{bmatrix}u_{11} & u_{12} & \cdots & u_{1,{g - 1}} & \cdots & u_{1p} \\u_{21} & u_{22} & \cdots & u_{2,{g - 1}} & \cdots & u_{2p} \\\vdots & \vdots & \cdots & \vdots & \cdots & \vdots \\u_{p,1} & u_{p,2} & \cdots & u_{p,{g - 1}} & \cdots & u_{p,p}\end{bmatrix}\Rightarrow U_{g - 1} \right. = \begin{bmatrix}u_{11} & u_{12} & \cdots & u_{1,{g - 1}} \\u_{21} & u_{22} & \cdots & u_{1,{g - 1}} \\\vdots & \vdots & \cdots & \vdots \\u_{p,1} & u_{p,2} & \cdots & u_{p,{g - 1}}\end{bmatrix}}} & (104)\end{matrix}$

[1110] Then, the feature projection, mapping measured space into featurespace, is obtained via right-multiplication by U_(g−)1.

[1111] The DAFE classification algorithm (step 1482 of FIG. 74) proceedsas follows. Let {circumflex over (T)}₁,{circumflex over (T)}₂, . . .,{circumflex over (T)}_(g) be the wavelength reduced, subsampledtraining (class data) matrices and Ŝ(λ) be the corresponding wavelengthreduced, subsampled test spectrum. The matrices {circumflex over(T)}_(j) and Ŝ(λ) are projected into feature space as follows:

{circumflex over (T)} _(j)

{circumflex over (T)} _(j) ·U _(g−1) ≡V _(j)

Ŝ(λ)

Ŝ(λ)·U _(g−1) ≡x  (105)

[1112] Next, the group mean vectors, group covariance matrices, andpooled within-groups covariance matrix are computed using the projectionmatrix, V_(j), in Equation 105, and using Equations 97, 98, and 101 asshown in Equations 106-108:

μ_(j)=mean(V_(j))  (106)

C _(j) =cov(V _(j))  (107) $\begin{matrix}{C_{pool} = {\frac{1}{n - g}{\sum\limits_{j = 1}^{g}\quad {\left( {n_{j} - 1} \right) \cdot C_{j}}}}} & (108)\end{matrix}$

[1113] Then, the Friedman matrix is calculated using the Friedmanparameters γ and λ according to Equation 109 as follows: $\begin{matrix}{{F\quad {r_{j}\left( {\gamma,\lambda} \right)}} = {{\left( {1 - \gamma} \right)\left\lbrack {{\left( {1 - \lambda} \right)C_{j}} + {\lambda \quad C_{pool}}} \right\rbrack} + {\frac{\gamma}{g - 1}{{{tr}\left\lbrack {{\left( {1 - \lambda} \right)C_{j}} + {\lambda \quad C_{pool}}} \right\rbrack} \cdot I_{{({g - 1})} \times {({g - 1})}}}}}} & (109)\end{matrix}$

[1114] In one embodiment, γ=0 and λ=0.5. Next, the Mahalanobis distance,d_(j)(x), is determined from the test spectrum to each data classaccording to Equation 110:

d _(j) ²(x)=(x−μ _(j))·Fr_(j) ⁻¹(γ,λ)·(x−μ_(j))^(T)  (110)

[1115] The Mahalanobis distance is a (1-by-1) number. Next, the Bayes'score is computed according to Equation 111:

br _(j)(x)=d _(j) ²(x)−2 ln(r _(j))+ln(|det(Fr _(j)(γ,λ)|)  (111)

[1116] The index j at which the minimum Bayes' score is attainedindicates the classification having the highest probability for the testpoint in question. The DAFE probability of classification for class jcan be computed for any of the g classifications according to Equation112: $\begin{matrix}\begin{matrix}{{{Prob}\left( {x \in {{Class}\quad j}} \right)} = \frac{\exp \left( {{- \frac{1}{2}}{{br}_{j}(x)}} \right)}{\sum\limits_{k = 1}^{g}{\exp \left( {{- \frac{1}{2}}{{br}_{k}(x)}} \right)}}} \\{= \frac{\frac{r_{j}}{{\det \left( {{Fr}_{j}\left( {\gamma,\lambda} \right)} \right)}} \cdot {\exp \left( {{- \frac{1}{2}}{d_{j}^{2}(x)}} \right)}}{\sum\limits_{k = 1}^{g}{\frac{r_{k}}{{\det \left( {{Fr}_{k}\left( {\gamma,\lambda} \right)} \right)}} \cdot {\exp \left( {{- \frac{1}{2}}{d_{k}^{2}(x)}} \right)}}}}\end{matrix} & (112)\end{matrix}$

[1117] DAFE classification probabilities are computed thusly for each ofthe interrogation points having a test reflectance spectrum, S(λ), thatis not eliminated in the Necrosis masking step (1424) or the hard“indeterminate” masking step (1426) in the embodiment shown in FIG. 74.

[1118] Step 1484 in FIG. 74 is the DASCO (discriminant analysis withshrunken covariances) method. Like the DAFE method of step 1482, theDASCO method reduces the dimensionality of the measured space bytransforming it into a lower dimensional feature space. DASCO differsfrom DAFE in that the feature space for the DASCO method is alongorthogonal directions of maximal variance, not (necessarily) maximaldiscrimination. Also, DASCO uses two Mahalanobis distances, not justone. The first distance is the distance to feature centers in primaryspace and the second distance is the distance to feature centers insecondary space.

[1119] In one embodiment, the DASCO method (step 1484) proceeds asfollows. First, a collection {T₁, T₂, . . . , T_(g)} of n_(j)-by-ptraining matrices is obtained from reference (training) broadbandarbitrated reflectance measurements. The amount of reflectance spectraldata obtained from a test region (interrogation point), as well as theamount of training data, are reduced by truncating the data sets toinclude only wavelengths between 400 nm and 600 nm.

[1120] Next, the training data and test data are scaled using meanscaling (mean centering) as follows: $\begin{matrix}{\left. T_{j}\mapsto{\left( {T_{j} - M_{j}} \right) \equiv Y_{j}} \right.,{{{where}\quad M_{j}} = \begin{bmatrix}\mu_{j} \\\mu_{j} \\\vdots \\\mu_{j}\end{bmatrix}_{n_{j} \times p}}} & (113)\end{matrix}$

 S(λ)

S(λ)−μ_(j) ≡S _(j)  (114)

[1121] where j=1, 2, . . . , g and g is the total number oftissue-class/state-of-health classes. The number of principal componentsin primary space is n_(p), and the number of principal components insecondary space is n_(s). The total number of components is n_(t). Inone embodiment, n_(p)=3, n_(s)=1, and n_(t)=4.

[1122] Next, the first n_(t) principal component loadings and scores arecomputed. This involves computing the singular value decomposition ofthe mean scaled training data matrix Y_(j) from Equation 113, asfollows:

Y _(j) =U _(j) D _(j) V _(j) ^(T)  (115)

[1123] A similar computation was made in Equation 104. Let V_(j,n) _(t)be the matrix comprised of the first n_(t) columns of V_(j). Theloadings and scores for Y_(j) are therefore indicated, respectively, inEquations 116 and 117, as follows:

Ld _(j) =V _(j,n) _(t)   (116)

sc _(j) =Y _(j) ·V _(j,n) _(t) ≡Y _(j) ·Ld _(j)  (117)

[1124] where Ld_(j) is a p-by-n_(t) matrix, and sc_(j) is ann_(j)-by-n_(t) matrix.

[1125] The next step in the DASCO method is to compute the class meanscores and covariances. First, the class mean vector in primary space,v_(j,p), and the class mean vector in secondary space, v_(j,s), arecomputed as follows:

v _(j)=mean(sc _(j)) (the mean is computed analogously to μ_(j) inEquation 97)  (118)

v _(j) ≡└v _(j,1) ,v _(j,2) , . . . ,v _(j,n) _(p) ,v _(j,n) _(p+1) ,v_(j,n) _(p+2) , . . . ,v _(j,n) _(p) _(+n) _(s) ┘≡└v _(j,p) ,v _(j,s) ,v_(j,s) ┘=v _(j,p) ⊕v _(j,s)  (119)

where v_(j,p)=└v_(j,1),v_(j,2), . . . ,v_(j,n) _(p) ┘ and v _(j,s) =└v_(j,n) _(p+1) ,v _(j,n) _(p+2) , . . . ,v _(j,n) _(p) _(+n) _(i)┘  (120)

[1126] Next, C_(j)=cov(sc_(j)) is defined as the class covariance matrixanalogous to that in Equation 100. In a manner similar to thecomputation of the primary and secondary space class mean vectors above,C_(j) is decomposed into the primary (C_(j,p)) and secondary (C_(j,s))space covariance matrices according to Equations 121-124 as follows:

C_(j)=C_(j,p)⊕C_(j,s)  (121) $\begin{matrix}{C_{j} =} & (122) \\\left\lbrack \quad \begin{matrix}{c_{11}(j)} & {c_{12}(j)} & \cdots & {c_{1,n_{p}}(j)} & {c_{1,{n_{p} + 1}}(j)} & {c_{1,{n_{p} + 2}}(j)} & \cdots & {c_{1,{n_{p} + n_{s}}}(j)} \\{c_{21}(j)} & {c_{22}(j)} & \cdots & {c_{2,n_{p}}(j)} & {c_{2,{n_{p} + 1}}(j)} & {c_{2,{n_{p} + 2}}(j)} & \cdots & {c_{2,{n_{p} + n_{s}}}(j)} \\\vdots & \vdots & \cdots & \vdots & \vdots & \vdots & \cdots & \vdots \\{c_{n_{t},1}(j)} & {c_{n_{t},2}(j)} & \cdots & {c_{n_{t},n_{p}}(j)} & {c_{n_{t},{n_{p} + 1}}(j)} & {c_{n_{t},{n_{p} + 2}}(j)} & \cdots & {c_{n_{t},{n_{p} + n_{s}}}(j)}\end{matrix}\quad \right\rbrack & \quad \\{C_{j,p} = \begin{bmatrix}{c_{11}(j)} & {c_{12}(j)} & \cdots & {c_{1,n_{p}}(j)} \\{c_{21}(j)} & {c_{22}(j)} & \cdots & {c_{2,n_{p}}(j)} \\\vdots & \vdots & \cdots & \vdots \\{c_{n_{t},1}(j)} & {c_{n_{t},2}(j)} & \cdots & {c_{n_{t},n_{p}}(j)}\end{bmatrix}} & (123) \\{C_{j,s} = \begin{bmatrix}{c_{1,{n_{p} + 1}}(j)} & {c_{1,{n_{p} + 2}}(j)} & \cdots & {c_{1,{n_{p} + n_{s}}}(j)} \\{c_{2,{n + 1}}(j)} & {c_{2,{n_{p} + 2}}(j)} & \cdots & {c_{2,{n_{p} + n_{s}}}(j)} \\\vdots & \vdots & \cdots & \vdots \\{c_{n_{t},{n_{p} + 1}}(j)} & {c_{n_{t},{n_{p} + 2}}(j)} & \cdots & {c_{n_{t},{n_{p} + n_{s}}}(j)}\end{bmatrix}} & (124)\end{matrix}$

[1127] Next, the scaled test spectrum from Equation 114 is projectedinto each principal component space according to Equation 125:

x(j)=Ld _(j) ·S _(j)  (125)

[1128] Then, x(j) is decomposed into primary and secondary space vectorsas follows:

x(j)≡[x _(l)(j),x₂(j), . . . ,x_(n) _(t) (j)]≡x_(j,p) ⊕x _(j,s)  (126)

[1129] where x_(j,p)=[x₁(j), x₂(j), . . . , x_(n) _(p) (j)] is theprojection of x(j) into primary space and x_(j,s)=[x_(n) _(p) ₊₁(j),x_(n) _(p) ₊₂(j), . . . , x_(n) _(p) _(+n) _(s) (j)] is the projectionof x(j) into secondary space.

[1130] The Mahalanobis distances in primary and secondary space arecomputed according to Equations 127 and 128 as follows:

d _(j,p) ²(x(j))=(x _(j,p) −v _(j,p))·C _(j,p) ⁻¹·(x _(j,p) −v_(j,p))^(T)  (127)

d _(j,s) ²(x(j))=(x _(j,s) −v _(j,s))·F _(j,s) ⁻¹·(x _(j,s) ^(−v)_(j,s))^(T)  (128)

[1131] where$F_{j,s} = {\frac{{tr}\left( \quad C_{j,s} \right)}{n_{s}} \cdot {I_{n_{s} \times n_{s}}.}}$

[1132] Then, the total distance is computed according to Equation 129 asfollows:

d(x(j))={square root}{square root over (d _(j,p) ²(x(j))+d _(js)²(x(j)))}  (129)

[1133] The DASCO probability of class assignment to class j is obtainedby computing the Bayes' score according to Equations 130 and 131 asfollows:

br(x(j))=d ²(x(j))−2 ln(r _(j))+ln(|det(C _(j,p))|)+n _(s)·ln(|det(Fr_(j,s))|)  (130) $\begin{matrix}{{{Prob}\left( {{x(j)} \in {{Class}\quad j}} \right)} = \frac{\exp \left( {{- \frac{1}{2}}{{br}_{j}\left( {x(j)} \right)}} \right)}{\sum\limits_{k = 1}^{g}{\exp \left( {{- \frac{1}{2}}{{br}_{k}\left( {x(k)} \right)}} \right)}}} & (131)\end{matrix}$

[1134] Equation 131 is evaluated for all classes j=1, 2, . . . g. DASCOclassification probabilities are computed thusly for each of theinterrogation points having a test reflectance spectrum, S(λ), that isnot eliminated in the Necrosis masking step (1424) or the hard“indeterminate” masking step (1426) in the embodiment shown in FIG. 74.

[1135] Probabilities determined using the DAFE classifier in step 1482of FIG. 74 and probabilities determined using the DASCO classifier instep 1484 are combined and normalized in step 1486 to obtain for eachinterrogation point a set of statistical probabilities that the pointbelongs, respectively, to one of a number oftissue-class/state-of-health categories. In one embodiment, there arefive classes, as described above, including the following: (1) Normalsquamous (N_(s)), (2) CIN 1 (C₁), (3) CIN 2/3 (C₂₃), (4) Metaplasia (M),and (5) Columnar (C_(ol)) tissue.

[1136] The probability matrices P_(DAFE) and P_(DASCO) containprobability vectors corresponding to the interrogation points in thescan pattern and are expressed as shown in Equations 132 and 133 asfollows: $\begin{matrix}{P_{DAFE} = \begin{bmatrix}{p_{{DAFE},1}(1)} & {p_{{DAFE},2}(1)} & \cdots & {p_{{DAFE},g}(1)} \\{p_{{DAFE},1}(2)} & {p_{{DAFE},2}(2)} & \cdots & {p_{{DAFE},g}(2)} \\\vdots & \vdots & \cdots & \vdots \\{p_{{DAFE},1}({nip})} & {p_{{DAFE},2}({nip})} & \cdots & {p_{{DAFE},g}({nip})}\end{bmatrix}} & (132) \\{P_{DASCO} = \left\lbrack \quad \begin{matrix}{p_{{DASCO},1}(1)} & {p_{{DASCO},2}(1)} & \cdots & {p_{{DASCO},g}(1)} \\{p_{{DASCO},1}(2)} & {p_{{DASCO},2}(2)} & \cdots & {p_{{DASCO},g}(2)} \\\vdots & \vdots & \cdots & \vdots \\{p_{{DASCO},1}({nip})} & {p_{{DASCO},2}({nip})} & \cdots & {p_{{DASCO},g}({nip})}\end{matrix}\quad \right\rbrack} & (133)\end{matrix}$

[1137] where g is the total number of classes (for example, g=5); nip isthe total number of interrogation points for which DAFE and DASCOprobabilities are calculated (for example, nip=up to 499); p_(DAFE,i)(j)represents the DAFE probability that the interrogation point j belongsto class i; and p_(DASCO,i)(j) represents the DASCO probability that theinterrogation point j belongs to class i.

[1138] Step 1486 of FIG. 74 represents the combination and normalizationof classification probabilities determined by the DAFE and DASCOclassifiers in steps 1482 and 1484, respectively. Thecombined/normalized probability matrix, P_(COMB), is obtained bymultiplying the probability matrices P_(DAFE) and P_(DASCO) (Equations134 and 135) element-wise and dividing the row-wise product by the sumof each row's elements.

Combining Spectral and Image Data

[1139] The block diagram of FIG. 74 includes steps representing thecombination of spectral masks and image masks (1468, 1470, 1472, 1474),as well as the application of the combined masks (1466, 1476, 1424,1478, 1480, 1424, 1426, 1428, 1430) in a tissue characterization system,according to one embodiment. These steps are discussed in more detailbelow.

[1140] As discussed above, the Necrosis_(spec) mask identifiesinterrogation points whose spectral data are indicative of necrotictissue. Since necrosis is one of the categories in which interrogationpoints are classified in step 1432 of FIG. 74, the Necrosis_(spec) maskis used not only to eliminate interrogation points from furtherprocessing, but also to positively identify necrotic regions. Therefore,it is necessary to filter out points affected by certain artifacts thatmay erroneously cause a positive identification of necrosis.

[1141] Step 1466 of FIG. 74 indicates that two image masks are appliedto the necrosis spectral mask—the smoke tube mask, [ST]_(vid), 1450 andthe speculum mask, [SP]_(vid) 1452. Regions in which a speculum or smoketube has been identified cannot be positively identified as necrotic.Thus, interrogation points having any portion covered by pixelsindicated by the smoke tube mask, [ST]_(vid), 1450 and/or the speculummask, [SP]_(vid), 1452 are identified as “Indeterminate” and areeliminated from the necrosis mask.

[1142] Following this treatment, the necrosis mask is then applied inthe broadband reflectance spectra classification sequence in step 1424of FIG. 74. Each interrogation point at which the necrosis mask appliesis classified as “Necrotic”. The broadband spectral data at theseinterrogation points are then eliminated from further processing, or,alternately, the results of the statistical classifiers at these pointsare ignored in favor of classification of the points as “Necrotic”.Similarly, the necrosis mask is applied in the NED (no evidence ofdisease) spectral classification sequence in step 1476 of FIG. 74. Eachinterrogation point at which the necrosis mask applies is classified as“Necrotic”. The NED_(spec) mask need not be computed for theseinterrogation points, or, alternately, the results of the NED_(spec)mask at these points may be ignored in favor of classification of thepoints as “Necrotic”.

[1143] Three image masks are combined to form a fluorescence hard mask,“F Hard,” which is applied in the NED (no evidence of disease) spectralclassification sequence in step 1478 of FIG. 74. As discussedhereinabove, hard masking results in a characterization of“Indeterminate” at affected interrogation points, and no furtherclassification computations are necessary for such points. The combinedfluorescence hard mask, “F Hard,” 1468 is a combination of the threeimage masks shown in FIG. 74 (1448, 1450, 1452), according to Equation134 as follows:

F Hard=[ROI]_(vid) OR [ST]_(vid) OR [SP]_(vid)  (134)

[1144] The combined “F Hard” mask is applied in the NED spectralclassification sequence in step 1478 of FIG. 74. Each interrogationpoint at which the “F Hard” mask applies is classified as“Indeterminate”. The NED_(spec) mask is not computed for theseinterrogation points. The “F Hard” mask applies for each interrogationpoint having any portion covered by pixels indicated by the “F Hard”combined image mask.

[1145] Two spectral masks and five image masks are combined to form abroadband reflectance “hard” mask, which is applied in the broadbandreflectance statistical classification sequence in step 1426 of FIG. 74.The combined hard mask, “BB Hard”, 1474 uses the image masks [ST]_(vid),[SP]_(vid), [ROI]_(vid), and [VW]_(vid) (1450, 1452, 1448, 1454) as hardmasks, and also treats them as “anchors” to qualify the sections of thetwo spectral masks—[CE]_(spec) and [MU]_(spec) (1444, 1446)—that areused as hard masks. The outer rim of interrogation points in thespectral pattern is also used as an anchor to the spectral masks.Finally, the intersection of the fluid-and-foam image mask [FL]_(vid)(1456) and the mucus spectral mask [MU]_(spec) (1446) is determined andused as a hard mask in “BB Hard” (1474). Each interrogation point atwhich the “BB Hard” mask applies is classified as “Indeterminate”. Thebroadband spectral data at these interrogation points are theneliminated from further processing, or, alternately, the results of thestatistical classifiers at these points are ignored in favor ofclassification of the points as “Indeterminate”.

[1146] In one embodiment, the combined hard mask, “BB Hard,” 1474 ofFIG. 74 is determined according to the following steps.

[1147] First, form a combined image processing hard mask IPHardIPMskusing all the interrogation points (IP's) that have any portion coveredby one or more of the followng image masks: [ST]_(vid), [SP]_(vid),[VW]_(vid) and [ROI]_(vid). The combined mask is expressed as:IPHardIPMsk=[ST]_(vid) OR [SP]_(vid) OR [VW]_(vid) OR [ROI]_(vid).Extend IPHardIPMsk to include the level one and level two neighbors ofthe interrogation points indicated above. For example, each IP that isnot on an edge has 6 level one neighbors and 12 level two neighbors, asshown in the scan pattern 202 in FIG. 5. Let extIMHardIPMsk be the newmask. Add all outer rim interrogation points to extIMHardIPMsk to formanchorMsk. The rim is defined by the following interrogation points forthe 499-point scan pattern 202 shown in FIG. 5: 1-9, 17-20, 31-33,47-48, 65-66, 84-85, 104-105, 125-126, 147-148, 170, 193, 215-216, 239,263,286-287, 309, 332, 354-355, 376-377, 397-398, 417-418, 436-437,454-455, 469-471, 482-485, 493-499. Form a combined spectral maskSpecIPMsk using all the interrogation points that are marked as either[CE]_(spec) or [MU]_(spec) (or both). Intersect the image processinganchor mask and the combined spectral mask to obtain SPHardMsk:SPHardMsk=anchorMsk AND SpecIPMsk. Intersect the image processing mask,[FL]_(vid), and spectral mucus mask, [MU]_(spec), to obtain the fluidhard mask FluidHardIPMsk, FluidHardIPMsk=[FL]_(vid) AND ([MU]_(spec) OR[CE]_(spec)). Finally form the final hard mask: BBHard=IPHardIPMsk ORSPHardMsk OR FluidHardIPMsk.

[1148] Two image masks—Blood_(vid) and OS_(vid) (1458, 1460)—arecombined to form a fluorescence “soft” mask, “F soft,” 1470 which isapplied in the NED spectral classification sequence in step 1480 of FIG.74. As discussed hereinabove, soft masking involves applying a weightingfunction to data from points identified by the mask in order to weightthe data according to the likelihood they are affected by an artifact.The mask “F soft” determines two weighting functions—pen_(blood)(IP) andpen_(os)(IP)—for interrogation points (IP's) that are at least partiallycovered by the image masks Blood_(vid) and Os_(vid) (1458, 1460). Asdiscussed hereinabove, a percentage coverage, α, is determined for eachinterrogation point according to the percentage of pixels correspondingto the interrogation point that coincide with the image mask. For theimage masks Blood_(vid) and Os_(vid), (1458, 1460), corresponding valuesα_(blood)(IP) and α_(os)(IP) are determined for each affectedinterrogation point, and Equations 135 and 136 are used to calculate thecorresponding weighting at these interrogation points:

pen _(blood)(IP)=1−α_(blood)(IP)  (135)

pen _(os)(IP)=1−α_(os)(IP)  (136)

[1149] The application of pen_(blood)(IP) and pen_(os)(IP) in the NEDspectral classification sequence of step 1480 is discussed in moredetail below.

[1150] Two image masks—Glare_(vid) and Mucus_(vid) (1462, 1464)—arecombined to form a broadband reflectance “soft” mask, “BB soft”, 1472which is applied in the broadband reflectance statistical classificationsequence in step 1428 of FIG. 74. As discussed hereinabove, soft maskinginvolves applying a weighting function to data from points identified bythe mask in order to weight the data according to the likelihood it isaffected by an artifact. The mask “BB soft” determines two weightingfunctions—pen_(glare)(IP) and pen_(mucus)(IP)—for interrogation points(IP's) that are at least partially covered by the image masksGlare_(vid) and Mucus_(vid) (1462, 1464). As discussed hereinabove, apercentage coverage, α, is determined for each interrogation pointaccording to the percentage of pixels corresponding to the interrogationpoint that coincide with the image mask. For the image masks Glare_(vid)and Mucus_(vid), (1462, 1464) corresponding values α_(glare)(IP) andα_(mucus)(IP) are determined for each affected interrogation point, andEquations 137 and 138 are used to calculate the corresponding penaltiesat these interrogation points:

pen _(glare)(IP)=1−{α_(glare)(IP)}^(1/5)  (137)

pen _(mucus)(IP)=1−α_(mucus)(IP)  (138)

[1151] The application of pen_(glare)(IP) and pen_(mucus)(IP) in thebroadband reflectance statistical classification sequence at step 1428is discussed in more detail below.

[1152] The tissue-class/state-of-health classification of interrogationpoints includes the application of masks as determined above. Thesesteps are shown in FIG. 74. The tissue-class/state-of-healthclassification method includes an NED (no evidence of disease) spectralclassification sequence, as well as a broadband reflectance statisticalclassification sequence, that apply the combined hard masks and softmasks described above. As discussed hereinabove, the separateidentification of necrotic regions and NED regions based on at leastpartially heuristic techniques allows for the development of astatistical classifier that concentrates on identifying tissue lessconducive to heuristic classification, for example, CIN 2/3 tissue.Furthermore, by eliminating data affected by artifacts, the statisticalclassifiers are further improved, leading to improved sensitivity andspecificity of the final classification of a tissue sample.

[1153] The Necrosis mask (1424, 1476), “BB Hard” mask (1426), and “FHard” mask (1478) are applied as shown in FIG. 74. Interrogation pointscoinciding with these masks are identified as either “Necrotic” or“Indeterminate”, as discussed hereinabove. In one embodiment, theseregions are removed from further consideration. The NED classificationsequence then applies the “F Soft” mask in step 1480. This is performedas explained below.

[1154] The NED_(spec) mask identifies interrogation points that indicatenormal squamous tissue, which is class (1) of the five classes used bythe DAFE and DASCO classifiers discussed previously. The NED_(spec) maskassigns at each indicated (masked) interrogation point a probabilityvector p_(s)=[1, 0, . . . , 0], where the normal squamous classificationprobability, N_(s) (class 1), is set equal to 1 and all other classprobabilities are set equal to 0. The “F Soft” mask is applied in step1480 by multiplying the N_(s) probability of indicated (masked) NEDinterrogation points by the product of the blood and os weightingfunctions, pen_(blood)(IP)·pen_(os)(IP). Hence, the normal squamousclassification probability, Ns, at these points will be less than 1.0.If the product, pen_(blood)(IP)·pen_(os)(IP), is equal to 0, then theinterrogation point IP is classified as “Indeterminate”. The NED_(spec)mask probability vector p_(s)=0 for all other interrogation points. Itis noted that if an interrogation point is not identified by theNED_(spec) mask, its N_(s) probability calculated by the broadbandreflectance statistical classification sequence is unaffected. Theapplication of the overall NED_(spec) mask is explained below in thediscussion of step 1430 in FIG. 74.

[1155] The broadband reflectance statistical classification sequenceapplies the Necrosis mask (1424) and the “BB Hard” mask (1426) beforedetermining statistical classification probabilities in steps 1482,1484, and 1486. As discussed above, the output of the broadbandstatistical classification is the probability matrix, P_(COMB), made upof probability vectors for the interrogation points, each vectorindicating respective probabilities that a given interrogation pointbelongs to one of the five tissue-class/state-of-health categories—(1)Normal squamous (N_(s)) (2) CIN 1 (C₁), (3) CIN 2/3 (C₂₃), (4)Metaplasia (M), and (5) Columnar (C_(ol)) tissue. The broadbandreflectance statistical classification sequence then applies the “BBSoft” mask in step 1428 by multiplying all five probabilities for eachaffected (masked) interrogation point by the quantitypen_(glare)(IP)·pen_(mucus(IP).)

[1156] Step 1432 of FIG. 74 classifies each interrogation point asNecrotic, CIN 2/3, NED, or Indeterminate. In one embodiment, theprobabilities in P_(COMB) that correspond to CIN 2/3 classification,p_(COMB,C23)(IP) [class 3], are considered indicative of “CIN 2/3”classification in step 1432, and all other classification categories inP_(COMB)—classes 1, 2, 4, and 5 (N_(s), C₁, M, and C_(ol))—areconsidered indicative of “NED” tissue. In an alternative embodiment,further classification distinctions are made in step 1432.

[1157] In step 1430 of FIG. 74, the results of the NED_(spec) mask areapplied to the broadband reflectance-based classifications, P_(COMB).The “Necrotic” interrogation points and the hard-masked “Indeterminate”points have been identified and removed before step 1430. In step 1430,the remaining interrogation points are either classified as“Indeterminate” or are assigned a value of CIN 2/3 classificationprobability, p_(C23)(IP). Here, p_(C23)(IP) is the CIN 2/3classification probability for interrogation point IP that is set as aresult of step 1430. Interrogation points that are not identified by theNED_(spec) mask have been assigned NED_(spec) mask probability vectorp_(s)=0, and p_(C23)(IP)=p_(COMB,C23)(IP) for these points.Interrogation points that are identified by the NED mask have p_(s)=[1,0, . . . , 0], or p_(s)=[{pen_(blood)(IP)·pen_(os)(IP)}, 0, . . . , 0],(where p_(s,Ns)(IP)=1 or pen_(blood)(IP)·pen_(os)(IP)) depending onwhether the point has been penalized or not by the “F Soft” mask in step1480. The following describes how values of p_(C23)(IP) are determinedfor interrogation pionts that are identified by the NED_(spec) mask:

[1158] Due to spectral arbitration in step 128 of FIG. 74, the broadbandsignal may have been suppressed for some interrogation points, and onlyfluorescence spectra are available. For these interrogation points, thefollowing rules are applied in step 1430 of FIG. 74:

[1159] 1. IF p_(s,Ns)(IP)>0, THEN p_(C23)(IP)=0.

[1160] 2. ELSE the interrogation point IP is classified as“Indeterminate”.

[1161] For points having a valid arbitrated broadband signal andfluorescence signal, the following fules are applied in step 1430 ofFIG. 74:

[1162] 1. IF p_(s,Ns)(IP)=1, THEN p_(C23)(IP)=0.

[1163] 2. IF p_(s,Ns)(IP)=0, THEN p_(C23)(IP)=p_(COMB,C23)(IP).

[1164] 3. IF p_(s,Ns)(IP)<1, THEN:

[1165] IF p_(s,Ns)(IP)<p_(COMB,Ns)(IP), THENp_(C23)(IP)=p_(COMB,C23)(IP),

[1166] ELSE, p_(C23)(IP)=0.

[1167] Step 1432 of FIG. 74 classifies each interrogation point asNecrotic, CIN 2/3, NED, or Indeterminate. Necrotic and hard-maskedIndeterminate interrogation points are identified prior to step 1430, asdescribed above. In step 1430, the remaining interrogation points areeither classified as Indeterminate or are assigned a value ofp_(C23)(IP). For these points, if p_(C23)(IP)=0, the point is classifiedas NED. If p_(C23)(IP)>0, the point is considered to have a non-zeroprobability of high grade disease (CIN 2/3). In one embodiment, diseasedisplay (step 138 of FIG. 74) uses these non-zero p_(C23)(IP) values todistinguish regions having low probability of CIN 2/3 and regions havinghigh probability of CIN 2/3.

[1168] Step 1434 of FIG. 74 represents post-classification processing.In one embodiment, this includes a final clean-up step to removeisolated CIN 2/3-classified interrogation points on the outer rim of thespectral scan pattern (for example, the outer rim consists of thenumbered interrogation points listed hereinabove. A CIN 2/3-classifiedinterrogation point is considered isolated if it has no direct, level-1neighbors that are classified as CIN 2/3. Such isolated points arere-classified as “Indeterminate” in step 1434 of FIG. 74.

Image Enhancement

[1169] The brightness of an acquired image of a tissue sample may changefrom patient to patient due to obstructions, tissue type, and otherfactors. As a result, some images may be too dark for adequate visualassessment. Step 126 of the tissue characterization system 100 of FIG. 1performs an image visual enhancement method to improve the image visualquality, using an image intensity transformation method. The improvedimage may then be used, for example, in the disease display of step 138of FIG. 1.

[1170] In one embodiment, the visual enhancement method of step 126 inFIG. 1 involves analyzing the histogram of the luminance values of aninput image, determining luminance statistics using only portions of theimage corresponding to tissue, and performing a piecewise lineartransformation to produce a visually enhanced image. Step 126 involvesusing the image masks, as shown in step 108 of FIGS. 1 and 73 and asdescribed previously, in order to determine which portions of the imageare used to compute the image statistics. Step 126 includes performingbrightness and contrast enhancement, as well as applying image featureenhancement to improve local image features such as edges, borders, andtextures of different tissue types. Finally, a color balancingcorrection is applied to reduce the redness in certain images.

[1171] The visual enhancement method of step 126 includes determiningwhich portions of the input tissue image correspond to tissue in theregion of interest, as opposed to artifacts such as glare, mucus, aspeculum, the os, blood, smoke tube, and/or areas outside the region ofinterest. Only the regions corresponding to tissue of interest are usedin determining luminance statistics used in performing the visualenhancement. In one embodiment, the image masks of FIGS. 73 and 74 areused to determine the portion of the image corresponding to tissue ofinterest. In one embodiment, this image portion is [tROI]_(vid), asubset of the [ROI]_(vid) mask, computed in Equation 139 as follows:[tROI]_(vid)=[ROI]_(vid)−{[Glare]_(vid)+[SP]_(vid)+[os]_(vid)+Blood_(vid)+Mucus_(vid)+[ST]_(vid)}(139)where the image masks above are as shown in FIG. 74 and as describedabove.

[1172] FIGS. 113A-C show graphs representing a step in a method of imagevisual enhancement in which a piecewise linear transformation of aninput image produces an output image with enhanced image brightness andcontrast. A histogram 2328 is computed for the luminance values μ (2326)of pixels within [tROI]_(vid) of an input image, and the histogram isused to determine parameters of a piecewise linear transformation shownin the plot 2324 of FIG. 113B. The transformation produces luminancevalues v (2330) of a corresponding brightness- and contrast-enhancedoutput image. The transformed image generally has a wider range ofluminance values, stretching from the minimum intensity (0) to themaximum intensity (255), than the input image. The luminance values fromthe input image are transformed so that input luminance values within agiven range of the mean luminance are stretched over a wider range ofthe luminance spectrum than input luminance at the extremes. In oneembodiment, the piecewise linear transformation is as shown in Equation140: $\begin{matrix}{v = \left\{ \begin{matrix}{{\alpha \quad \mu},} & {L_{\min} \leq \mu < \mu_{a}} \\{{{\beta \left( {\mu - \mu_{a}} \right)} + v_{a}},} & {\mu_{a} \leq \mu < \mu_{b}} \\{{{\gamma \left( {\mu - \mu_{b}} \right)} + v_{b}},} & {\mu_{b} \leq \mu < L_{\max}}\end{matrix} \right.} & (140)\end{matrix}$

[1173] where L_(max) is the maximum luminance value of a pixel within[tROI]_(vid) of the input image; the parameters μ_(a), μ_(b), v_(a), andv_(b) are piecewise linear breakpoints; and α,β, and γ are slopes of thetransformation.

[1174] In one embodiment, the image brightness and contrast enhancementis performed according to the following steps. First, calculate theluminance L from the RGB signal of the input image using the formula:L=0.299*R+0.587*G+0.114*B. Extract the luminance image LROI within tROI([tROL]_(vid)): LROI=L AND tROI. Compute LROI mean, IMean.Compute thepiecewise linear breakpoints ma, mb, na, nb (μ_(a), μ_(b), v_(a), andv_(b)) from the LROI histogram, nHist[ ], as follows:

[1175] 1. If ((IMean>38) AND (IMean<132)):

[1176] a. Compute and normalize nHist[ ] to the range [0, 1].

[1177] b. Compute ma and mb, the 5% and 98% histogram tails:

[1178] ma=i, if sum(nHist [i])>0.05, i=0 to 255.

[1179] mb=i, if sum(nHist [i])>0.98, i=0 to 255.

[1180] c. Define the expected low and high intensity parameter na andnb:

[1181] d. na=0 and nb=180.

[1182] 2. If(IMean>38 AND (IMean<132) AND ((ma≧na AND ma<100 ANDnb>20)), compute the slope or the degree of enhancement,

[1183] bcDOE:

[1184] bcDOE=(nb−na)/(mb−ma).

[1185] 3. If ((IMean>38) AND (IMean<132)), apply brightness and contrastenhancement transformation to input color image in RGB to obtain bcRGB(brightness and contrast enhanced color image).

[1186] In addition to producing an output image with enhanced imagebrightness and contrast, the visual enhancement method of step 126(FIG. 1) also includes performing an image feature (local contrast)enhancement of the output image to emphasize high frequency componentssuch as edges and fine features for the purposes of visual inspection.In one embodiment, image feature enhancement is performed using aspatial filtering technique according to Equations 141 and 142 asfollows:

I _(out)(m,n)=I _(in)(m,n)+ρG(m,n)  (141)

G(m,n)=I _(in)(m,n)−S(m,n)  (142)

[1187] where G(m, n) is the gradient image; ρ is the degree of theenhancement; I_(in)(m, n) and I_(out)(m, n) are the original and theresultant image of the feature enhancement operation; and S(m, n) is thesmoothed (lowpass filtered) version of I_(in)(m, n).

[1188] In one embodiment, the image feature enhancement operation of thevisual enhancement method of step 126 is performed according to thefollowing steps:

[1189] If IMean>38:

[1190] 1. Smooth bcRGB (brightness and contrast enhanced color image)with a 7×7 boxcar filter to obtain smRGB.

[1191] 2. Subtract smRGB from bcRGB to obtain the gradient image, grRGB.

[1192] 3. Dilate glareMsk twice to obtain dGlareMsk=dil (glareMsk, 2).

[1193] 4. Remove dilated glare regions form gradient image to avoidemphasizing glare regions:

[1194] a. Convert gray image dGlareMsk to RGB image, dGlareMskC.

[1195] b. Remove glare image from gradient image to obtain grRGBgI:

[1196] grRGBgI=grRGB−dGlareMskC.

[1197] 5. Define the degree of feature enhancement, feDOE, fromexperiments, feDOE=0.8.

[1198] 6. Scale grRGBgI by feDOE to obtain feRGB.

[1199] 7. Add feRGB to bcRGB to produce image feature enhanced imagefRGB.

[1200] In addition to producing an output image with enhanced imagebrightness, contrast, and image features, the visual enhancement methodof step 126 (FIG. 1) also includes performing color balancing to reduceredness in certain overly-red tissue images, based on amean-red-to-mean-blue ratio.

[1201] In one embodiment, the color balancing operation of the visualenhancement method of step 126 is performed according to the followingsteps:

[1202] If IMean >38:

[1203] 1. Split RGB (i.e. of the image feature enhanced image fRGB) intoR, G, B.

[1204] 2. Extract the R image (within the tROIMsk) and compute meantissue redness, tRed.

[1205] 3. Extract the B image (within the tROIMsk) and compute meantissue blueness tBlue.

[1206] 4. Compute the red-blue ratio as RBRat=tRed/tBlue.

[1207] 5. Perform color balancing: If RBRat < 1.20, no red redection.Else if RBRat>=1.20 AND RBRat < 1.32, R = 0.95* R. Else if RBRat>=1.32AND RBRat < 1.55, R = 0.90* R. Else if RBRat>=1.55, R = 0.85*v.

[1208] 6. Combine the R, G and B channels to form the final color imagefor display.

Diagnostic Display

[1209] In one embodiment, the tissue characterization system 100 of FIG.1 comprises producing a disease probability display 138 for a reference(base) image of a test tissue sample using the interrogation pointclassifications in step 1432 of FIG. 74—Necrotic, CIN 2/3, NED, andIndeterminate. A method of disease probability display 138 includesproducing an output overlay image with annotations for indeterminateregions, necrotic regions, and/or regions of low-to-high probability ofhigh-grade disease, according to the classifications determined in step1432 of FIG. 74 for a given patient scan. The annotations are shown asan overlay on top of the reference tissue image to provideeasily-discernible tissue classification results, for example,indicating regions of concern for the purposes of biopsy, treatment,diagnosis, and/or further examination.

[1210] In one embodiment, indeterminate regions are indicated by a gray“see-through” crosshatch pattern that only partially obscures theunderlying reference image. Necrotic regions are indicated by a greentrellis pattern. Regions of tissue associated with high-grade disease(for example, CIN 2/3) are indicated by patches of contrasting colorwhich intensify according to the likelihood of high-grade disease.

[1211] In one embodiment, the disease probability display method 138 ofFIG. 74 as applied to a reference image of tissue from a patient scanincludes the following steps: determining a disease display layer fromthe classification results of step 1432, overlaying the disease displaylayer on the reference image, determining an “indeterminate” mask fromthe classification results, overlaying the indeterminate mask on thedisease display image using a gray crosshatch pattern, determining a“necrosis” mask from the classification results, and overlaying thenecrosis mask on the disease display image using a green trellispattern. The result of the disease probability display method 138 ofFIG. 74 is a state-of-health “map” of the tissue sample, withannotations indicating indeterminate regions, necrotic regions, and/orregions of low-to-high probability of high-grade disease.

[1212]FIG. 114B represents an exemplary image of cervical tissue 2358obtained during a patient examination and used as a reference (base)image in constructing an output overlay image in the disease probabilitydisplay method 138 in FIG. 74. FIG. 114B shows the output overlay image2360 produced by the disease probability display method 138 in FIG. 74that corresponds to the reference image 2358 in FIG. 114A. The outputoverlay image 2360 in FIG. 114B contains annotations indicatingindeterminate regions (2366), regions associated with a low probabilityof CIN 2/3 (2362), and regions associated with a high probability of CIN2/3 (2364).

[1213] The disease probability display method 138 begins with thedetermination of a disease display layer from the CIN 2/3 classificationresults of step 1432 in FIG. 74. In step 1432, values of p_(C23)(IP) aredetermined for interrogation points having a non-zero probability ofhigh-grade disease (here, CIN 2/3). An area of tissue indicative ofhigh-grade disease is represented on the disease display layer as anarea whose color varies from yellow-to-blue, depending on values ofp_(C23)(IP) at corresponding interrogation points. The yellow colorrepresents low probability of high-grade disease, and the blue colorrepresents high probability of high-grade disease. At the low end of theprobability range, the yellow color is blended into the reference imageso that there is no sharp discontinuity between the high-grade diseaseregion and the image. In one embodiment, a minimum cut-off probability,p_(C23) min(IP), is set so that interrogation points with values ofp_(C23)(IP) lower than the minimum cut-off do not show on the diseasedisplay layer. In one embodiment, p_(C23min)(IP)=0.2.

[1214]FIGS. 115A and 115B represent two stages in the creation of adisease display layer, according to one embodiment. FIG. 115A shows thedisease display layer 2368 wherein high-grade disease probabilities arerepresented by circles with intensities scaled by values of p_(C23)(IP)at corresponding interrogation points. In order to more realisticallyrepresent regions of high-grade disease on the tissue sample, thecircles in FIG. 115A are replaced with cones, then filtered to producethe disease display layer 2372 shown in FIG. 115B.

[1215] Finally, the grayscale intensity values are converted to a colorscale so that regions of high-grade disease appear on the overlay imageas patches of contrasting color that intensify according to thelikelihood of disease.

[1216] In one embodiment, the disease probability display method 138 ofFIG. 1 includes creating a disease display layer according to thefollowing steps:

[1217] 1. Retrieve the reference image (base image).

[1218] 2. If all IPs are indeterminate, skip to creating theIndeterminate Mask.

[1219] 3. Generate CIN 2/3 probability image, I_(p), of base image size,for all non-indeterminate IPs:

[1220] a. Generate a regular truncated cone centered at (15,15) on asquare matrix of size 29-by-29, set to 0:

[1221] i. The two truncating circles are centered around (15,15) andhave a radius R₀=14 and R_(i)=6.

[1222] ii. For each cone point, cone(i, j), let R be the distance fromthe geometric center (15,15).

[1223] 1. If R>R₀, cone(i,j)=0.

[1224] 2. If R<R_(i), cone(i,j)=1.

[1225] 3. If R_(i)<=R<=R₀, cone(i,j)=(R₀−R)/(R₀−R₁).

[1226] b. Initialize I_(p) to 0.

[1227] c. For each IP with probability p_(C23)(IP)≧0.2:

[1228] i. make a copy of the cone;

[1229] ii. scale it by p;

[1230] iii. add it to I_(p) with the cone's center aligned with the IPlocation.

[1231] d. Smooth I_(p) using a 33 by 33 separable symmetric Hammingwindow filter specified by:

[1232] i. the following coefficients (since the filter is symmetricaround the origin, only 17 coefficients are specified below; the othersare the mirror image around 1.0): (0.0800, 0.0888, 0.1150, 0.1575,0.2147, 0.2844, 0.3640, 0.4503 0.5400, 0.6297, 0.7160, 0.7956, 0.8653,0.9225, 0.965, 0.9912, 1.0);

[1233] ii. a gain of (0.85/301.37)^(1/2) for the 33 point ID filter.

[1234] e. Linearly rescale I_(p) from the [0.21] range to the [01]range.

[1235] f. Clip rescaled I_(p) to range [0 1].

[1236] 4. Compute an RGB colormap image and an alpha blending channelfrom the probability image I_(p). The colormap defines a transformationfrom integer intensity values in the range [0,255] to an RGBα image.

[1237] a. The R colormap is a piecewise linear map specified by thefollowing breakpoints [0,255], [97,220], [179,138] and [255,0].

[1238] b. The G colormap is a piecewise linear map specified by thefollowing breakpoints [0,0], [81,50], [210,162] and [255,92].

[1239] c. The B colormap is a piecewise linear map specified by thefollowing breakpoints [0,255], [120,225], [178,251] and [255,255].

[1240] d. The α colormap is a piecewise linear map specified by thefollowing breakpoints [0,255], [120,225], [178,251] and [255,255].

[1241] e. Convert the floating point I_(p) image to an 8-bit image, inthe range [0,225] by rounding the product of each I_(p) image pixel by255.

[1242] f. Use the tissue colormap to get RGBα pixel values for thedisease display layer.

[1243]FIG. 116 shows the color transformation used in overlaying thedisease display layer onto the reference image, as in the overlay image2360 of FIG. 114B. The first colorbar 2374 in FIG. 116 shows the blendedcolors from yellow to blue that correspond to values of diseaseprobability p_(C23)(IP), depicted on the x-axis 2375. A colorcorresponding to the average tissue color is determined, as shown incolorbar 2378. The average tissue color is blended into theprobability-correlated yellow-to-blue colorbar 2374 so that the yellowcolor is blended into the reference image where the disease probability,as indicated by the filtered disease display layer, is low. This avoidsa sharp discontinuity between the disease map and the tissue. In oneembodiment, the disease display layer and the base (reference) image arecombined by using alpha-channel blending, where the alpha channel is asshown in step #4 of the above method to create a disease display layer.The disease display layer is overlaid upon the base image with blendingcontrolled by the computed alpha channel values according to Equation143 as follows:

(Overlay Image Pixel)=α·(Disease Display Layer Pixel)+(1−α)·(Base ImagePixel)  (143)

[1244] Next, the disease probability display method 138 of FIG. 1includes determining an “indeterminate” mask from the classificationresults in step 1432 of FIG. 74, where indeterminate regions areindicated by a gray “see-through” crosshatch pattern. For an exemplaryreference image, interrogation points classified as “Indeterminate” instep 1432 of FIG. 74 indicate where the indeterminate mask is activated.The indeterminate crosshatch mask is then combined with the outputoverlay image, as is shown in the overlay image 2360 of FIG. 114B. Here,indeterminate regions 2366 are indicated in shadowed regions around theedge of the tissue sample.

[1245] In one embodiment, the disease probability display method 138 ofFIG. 1 includes creating an indeterminate crosshatch mask according tothe following steps:

[1246] 1. Create image, msk, of base image size and set to 0.

[1247] 2. Draw disks of radius 0.75 mm centered at the coordinate ofeach indeterminate interrogation point.

[1248] 3. Erode mask image 3 times to obtain erodMsk=erod (msk, 3).

[1249] 4. Compute image binary perimeter, perMsk, of erodMsk:

[1250] perMsk=not (erod (erodMsk, 1)) AND (erodMsk)), 1).

[1251] 5. Compute indeterminate crosshatch mask:

[1252] a. Retrieve crosshatch image, xhatch, defined by a horizontalpitch of 10 pixels, a vertical pitch of 20 pixels, a crosshatch slope of2 and a grey value of (166,166,166).

[1253] b. Perform logical OR of erodMsk and xhatch to obtain xhatchMsk.

[1254] c. Perform logical OR of xhatchMsk with perMsk.

[1255] Next, the disease probability display method 138 of FIG. 1includes determining a “necrosis” mask from the classification resultsin step 1432 of FIG. 74, where necrotic regions are indicated by a green“see-through” trellis pattern. FIG. 117A depicts an exemplary referenceimage 2388 of cervical tissue having necrotic regions. For an exemplaryreference image, interrogation points classified as “Necrotic” in step1432 of FIG. 74 indicate where the “necrosis” mask is activated. Anecrosis trellis mask is included in the overlay image, as is shown inthe overlay image 2396 of FIG. 117B.

[1256] In one embodiment, the disease probability display method 138 ofFIG. 1 includes creating a necrosis trellis mask according to thefollowing steps:

[1257] 1. Create image, msk, of base image size, and set it to 0.

[1258] 2. Draw disks of radius 0.75 mm centered at the coordinate ofeach necrotic tissue interrogation point.

[1259] 3. Erode mask image 3 times to obtain erodMsk=erod (msk, 3).

[1260] 4. Compute image binary perimeter, perMsk, of erodMsk:

[1261] perMsk=not (erod (erodMsk, 1)) AND (erodMsk)), 1).

[1262] 5. Compute necrotic tissue trellis mask:

[1263] a. Retrieve trellis image, trellis, defined by a horizontal pitchof 8 pixels, a vertical pitch of 8 pixels, a line thickness of 2 and agreen value of (0,255,104).

[1264] b. Perform logical OR of erodMsk and xhatch to obtain trellisMsk.

[1265] c. Perform logical OR of trellisMsk with perMsk.

[1266] The result of the disease probability display method 138 of FIG.74 is a state-of-health “map” of a tissue sample, with annotationsindicating indeterminate regions, necrotic regions, and/or regions oflow-to-high probability of high-grade disease. The disease displayoverlay images contain indeterminate regions and regions of low-to-highprobability of CIN 2/3.

[1267] In one embodiment, the disease display overlay image is producedimmediately following a patient scan in which spectral and image dataare acquired and processed. This allows a physicial to provideon-the-spot diagnostic review immediately following the scan.

[1268] Equivalents

[1269] While the invention has been particularly shown and describedwith reference to specific preferred embodiments, it should beunderstood by those skilled in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the invention as defined by the appended claims.

What is claimed is:
 1. A method of determining a tissue-classprobability for a region of tissue, the method comprising the steps of:(a) processing a first set of spectral data from a region of tissue toobtain a first measure of tissue-class probability for said region oftissue, wherein said first set comprises reflectance spectral data; (b)processing a second set of spectral data from said region to obtain asecond measure of tissue-class probability for said region; and (c)determining an overall tissue-class probability for said region usingsaid first measure and said second measure.
 2. The method of claim 1,wherein tissue-class probability is a probability that said regioncomprises tissue of a predetermined type, wherein said type is selectedfrom the group consisting of CIN 1, CIN 2, CIN 3, CIN 2/3, normalsquamous, normal columnar, necrosis, NED, metaplasia, and cancer.
 3. Themethod of claim 1, wherein at least one of said first processing stepand said second processing step comprises using a statistical methodbased on maximal variance.
 4. The method of claim 1, wherein at leastone of said first processing step and said second processing stepcomprises using a statistical method based on maximal discrimination. 5.The method of claim 1, wherein said first processing step comprisesusing a statistical method based on maximal variance and said secondprocessing step comprises using a statistical method based on maximaldiscrimination.
 6. The method of claim 1, wherein at least one of saidfirst processing step and said second processing step comprisesperforming a principal component analysis.
 7. The method of claim 1,wherein at least one of said first processing step and said secondprocessing step comprises performing a feature coordinate extraction. 8.The method of claim 1, wherein at least one of said first processingstep and said second processing step comprises performing a discriminantanalysis with shrunken covariances.
 9. The method of claim 1, wherein atleast one of said first processing step and said second processing stepcomprises performing a discriminant analysis feature extraction.
 10. Themethod of claim 1, wherein said first processing step comprisesperforming a discriminant analysis with shrunken covariances and saidsecond processing step comprises performing a discriminant analysisfeature extraction.
 11. The method of claim 1, wherein at least one ofsaid first processing step and said second processing step comprisesdetermining a statistical distance.
 12. The method of claim 11, whereinsaid statistical distance is selected from the group consisting of aMahalanobis distance, a Bhattacharya distance, a Euclidian distance, anda Jeffrey-Matsushita distance.
 13. The method of claim 1, wherein atleast one of said first processing step and said second processing stepcomprises determining a statistical distance to feature centers inprimary space and a statistical distance to feature centers in secondaryspace.
 14. The method of claim 1, wherein at least one of said firstprocessing step and said second processing step comprises determining aBayes score.
 15. The method of claim 1, wherein said first set and saidsecond set share at least one member.
 16. The method of claim 1, whereinsaid first set and said second set are identical.
 17. The method ofclaim 1, wherein said first set and said second set comprise reflectancespectral data.
 18. The method of claim 1, wherein at least one of saidfirst set and said second set comprises fluorescence spectral data. 19.The method of claim 1, wherein at least one of said first set and saidsecond set comprises data corresponding to wavelengths between about 370nm and about 650 nm.
 20. The method of claim 1, wherein said first setof spectral data consists of data corresponding to wavelengths betweenabout 400 nm and about 600 nm.
 21. The method of claim 1, wherein saidsecond set of spectral data consists of data corresponding towavelengths between about 370 nm and about 650 nm.
 22. The method ofclaim 1, wherein at least one of said first set and said second setcomprises preprocessed spectral data.
 23. The method of claim 22,wherein said preprocessed spectral data comprise data that are filteredto remove members that are non-representative of said region.
 24. Amethod of determining the condition of a region of tissue, the methodcomprising: (a) for each of a plurality of predefined tissue classes,processing reflectance spectral data obtained from a region of tissue todetermine a first and a second measure of probability that said regioncomprises tissue within said class; and (b) determining a condition ofsaid region using said first and said second measures.
 25. The method ofclaim 24, wherein said condition is selected from the group consistingof CIN 2/3, NED, indeterminate, and necrotic.
 26. The method of claim24, wherein one or more members of said plurality of predefined tissueclasses are selected from the group consisting of CIN 1, CIN 2, CIN 3,CIN 2/3, NED, normal squamous, normal columnar, metaplasia, and cancer.27. The method of claim 24, wherein said first processing step comprisesusing a principal component analysis method to determine said firstmeasure of probability and a feature coordinate extraction method todetermine said second measure of probability.
 28. The method of claim24, wherein said first processing step comprises comparing spectral dataobtained from said region with two or more sets of training data. 29.The method of claim 24, wherein said second processing step comprisesdetermining an overall probability that said region comprises tissuewithin said class, using said first and said second measures.
 30. Themethod of claim 29, wherein said overall probability is weightedaccording to a likelihood that said region lies within a zone ofinterest.
 31. The method of claim 29, wherein said overall probabilityis weighted according to a likelihood that spectral data obtained fromsaid region are affected by an obstruction.
 32. A method ofcharacterizing the condition of a region of tissue, the methodcomprising the steps of: (a) processing spectral data obtained from aregion of tissue to determine, for each member of a plurality ofpredefined tissue classes, a probability that said region comprisestissue within said member; (b) evaluating a classification metric usingspectral data obtained from said region; (c) if said classificationmetric is satisfied, characterizing a condition of said region accordingto said classification metric; and (d) if said classification metric isnot satisfied, characterizing a condition of said region according tosaid probabilities.
 33. The method of claim 32, wherein said evaluatingstep comprises using fluorescence spectral data.
 34. The method of claim32, wherein said processing step comprises processing reflectancespectral data.
 35. The method of claim 32, wherein said evaluating stepcomprises using fluorescence spectral data and said processing stepcomprises processing reflectance spectral data.
 36. The method of claim32, wherein said processing step comprises applying one or morestatistical methods to a set of reflectance spectral data obtained fromsaid tissue.
 37. The method of claim 32, wherein said classificationmetric comprises a non-statistically-based component.
 38. The method ofclaim 37, wherein said non-statistically-based component is indicativeof a substance present in tissue within at least one of said predefinedtissue classes.
 39. The method of claim 38, wherein said substance isselected from the group consisting of collagen, porphyrin, FAD, andNADH.
 40. The method of claim 32, wherein said classification metriccomprises one or more statistically-based components and one or morenon-statistically-based components.